diff --git a/student.ipynb b/student.ipynb
index d3bb34af..725d6bcb 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -4,23 +4,6986 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Final Project Submission\n",
+    "### Group 7 Project : Analyzing King County House Sales Data. \n",
     "\n",
-    "Please fill out:\n",
-    "* Student name: \n",
-    "* Student pace: self paced / part time / full time\n",
-    "* Scheduled project review date/time: \n",
-    "* Instructor name: \n",
-    "* Blog post URL:\n"
+    "#### Students:\n",
+    "\n",
+    "- **Jeremiah Waiguru**\n",
+    "\n",
+    "- **Olive Muloma**\n",
+    "\n",
+    "- **Troye Gilbert**\n",
+    "\n",
+    "- **Josephine Maro**\n",
+    "\n",
+    "         Student pace: FULL TIME HYBRID\n",
+    "         Scheduled project review date/time: N/A\n",
+    "         Instructor name: MARYANN MWIKALI\n",
+    "         Blog post URL: N/A\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Introduction\n",
+    "The aim of this project is to analyze the King County House Sales dataset, which comprises data from house sales in King County, spanning the years 2014 to 2015. This dataset provides a comprehensive view of various factors influencing house prices in the region, including location, size, condition, and other relevant attributes. By exploring and analyzing this dataset, we seek to gain insights into the factors that drive house prices in King County and to build predictive models to estimate house prices accurately. \n",
+    "\n",
+    "We use an iterative process to prepare and model our data while utilizing exploratory data analysis to better understand our dataset. Next, we try to iterate across models using various statistical techniques in order to identify the model with the best statistical R-Squared Value, RMSE, and differential values. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Objectives:\n",
+    "\n",
+    "- Explore and understand the structure and content of the King County House Sales dataset.\n",
+    "- Perform descriptive analysis to gain insights into key features, trends, and patterns in the data.\n",
+    "- Identify factors that significantly influence house prices in King County.\n",
+    "- Build predictive models to estimate house prices based on relevant features and attributes.\n",
+    "- Evaluate and validate the performance of the predictive models using appropriate metrics.\n",
+    "- Provide actionable insights and recommendations based on the analysis to stakeholders."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Methodology \n",
+    "1. Business Understanding\n",
+    "2. Data Understanding\n",
+    "3. Exploratory Data Analysis\n",
+    "4. Pre-processing\n",
+    "5. Modelling\n",
+    "6. Results Interpretation\n",
+    "7. Recommendations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data\n",
+    "We have been provided with a dataset with house sale prices in King County, Washington State, USA from 2014 to 2015 to use for this project.\n",
+    "\n",
+    "A dataset has been provided and can be found in the kc_house_data.csv file in this repository.\n",
+    "\n",
+    "The column names and descriptions as provided can be found in the column_names.md file in this repository.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import the necessary libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib as plt\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import statsmodels.formula.api as sfm\n",
+    "import statsmodels.api as sm\n",
+    "import scipy.stats as stats\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### loading dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>10/13/2014</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>2/25/2015</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>2/18/2015</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+       "id                                                                             \n",
+       "7129300520  10/13/2014  221900.0         3       1.00         1180      5650   \n",
+       "6414100192   12/9/2014  538000.0         3       2.25         2570      7242   \n",
+       "5631500400   2/25/2015  180000.0         2       1.00          770     10000   \n",
+       "2487200875   12/9/2014  604000.0         4       3.00         1960      5000   \n",
+       "1954400510   2/18/2015  510000.0         3       2.00         1680      8080   \n",
+       "\n",
+       "            floors waterfront  view  condition          grade  sqft_above  \\\n",
+       "id                                                                          \n",
+       "7129300520     1.0        NaN  NONE    Average      7 Average        1180   \n",
+       "6414100192     2.0         NO  NONE    Average      7 Average        2170   \n",
+       "5631500400     1.0         NO  NONE    Average  6 Low Average         770   \n",
+       "2487200875     1.0         NO  NONE  Very Good      7 Average        1050   \n",
+       "1954400510     1.0         NO  NONE    Average         8 Good        1680   \n",
+       "\n",
+       "           sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
+       "id                                                                            \n",
+       "7129300520           0.0      1955           0.0    98178  47.5112 -122.257   \n",
+       "6414100192         400.0      1951        1991.0    98125  47.7210 -122.319   \n",
+       "5631500400           0.0      1933           NaN    98028  47.7379 -122.233   \n",
+       "2487200875         910.0      1965           0.0    98136  47.5208 -122.393   \n",
+       "1954400510           0.0      1987           0.0    98074  47.6168 -122.045   \n",
+       "\n",
+       "            sqft_living15  sqft_lot15  \n",
+       "id                                     \n",
+       "7129300520           1340        5650  \n",
+       "6414100192           1690        7639  \n",
+       "5631500400           2720        8062  \n",
+       "2487200875           1360        5000  \n",
+       "1954400510           1800        7503  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# loading the data and previewing the dataframe\n",
+    "df = pd.read_csv('data/kc_house_data.csv', index_col=0)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preparation\n",
+    "In this section, we shall be preparing the data for further processing and modelling\n",
+    "\n",
+    "### Investigate data types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(21597, 20)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# shape of our data\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2.159700e+04</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>2.159700e+04</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>17755.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.402966e+05</td>\n",
+       "      <td>3.373200</td>\n",
+       "      <td>2.115826</td>\n",
+       "      <td>2080.321850</td>\n",
+       "      <td>1.509941e+04</td>\n",
+       "      <td>1.494096</td>\n",
+       "      <td>1788.596842</td>\n",
+       "      <td>1970.999676</td>\n",
+       "      <td>83.636778</td>\n",
+       "      <td>98077.951845</td>\n",
+       "      <td>47.560093</td>\n",
+       "      <td>-122.213982</td>\n",
+       "      <td>1986.620318</td>\n",
+       "      <td>12758.283512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.673681e+05</td>\n",
+       "      <td>0.926299</td>\n",
+       "      <td>0.768984</td>\n",
+       "      <td>918.106125</td>\n",
+       "      <td>4.141264e+04</td>\n",
+       "      <td>0.539683</td>\n",
+       "      <td>827.759761</td>\n",
+       "      <td>29.375234</td>\n",
+       "      <td>399.946414</td>\n",
+       "      <td>53.513072</td>\n",
+       "      <td>0.138552</td>\n",
+       "      <td>0.140724</td>\n",
+       "      <td>685.230472</td>\n",
+       "      <td>27274.441950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>7.800000e+04</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>5.200000e+02</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>1900.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98001.000000</td>\n",
+       "      <td>47.155900</td>\n",
+       "      <td>-122.519000</td>\n",
+       "      <td>399.000000</td>\n",
+       "      <td>651.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.220000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.750000</td>\n",
+       "      <td>1430.000000</td>\n",
+       "      <td>5.040000e+03</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1190.000000</td>\n",
+       "      <td>1951.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98033.000000</td>\n",
+       "      <td>47.471100</td>\n",
+       "      <td>-122.328000</td>\n",
+       "      <td>1490.000000</td>\n",
+       "      <td>5100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>4.500000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.250000</td>\n",
+       "      <td>1910.000000</td>\n",
+       "      <td>7.618000e+03</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>1560.000000</td>\n",
+       "      <td>1975.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98065.000000</td>\n",
+       "      <td>47.571800</td>\n",
+       "      <td>-122.231000</td>\n",
+       "      <td>1840.000000</td>\n",
+       "      <td>7620.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.450000e+05</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>2550.000000</td>\n",
+       "      <td>1.068500e+04</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2210.000000</td>\n",
+       "      <td>1997.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98118.000000</td>\n",
+       "      <td>47.678000</td>\n",
+       "      <td>-122.125000</td>\n",
+       "      <td>2360.000000</td>\n",
+       "      <td>10083.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.700000e+06</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>13540.000000</td>\n",
+       "      <td>1.651359e+06</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>9410.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>98199.000000</td>\n",
+       "      <td>47.777600</td>\n",
+       "      <td>-121.315000</td>\n",
+       "      <td>6210.000000</td>\n",
+       "      <td>871200.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\n",
+       "count  2.159700e+04  21597.000000  21597.000000  21597.000000  2.159700e+04   \n",
+       "mean   5.402966e+05      3.373200      2.115826   2080.321850  1.509941e+04   \n",
+       "std    3.673681e+05      0.926299      0.768984    918.106125  4.141264e+04   \n",
+       "min    7.800000e+04      1.000000      0.500000    370.000000  5.200000e+02   \n",
+       "25%    3.220000e+05      3.000000      1.750000   1430.000000  5.040000e+03   \n",
+       "50%    4.500000e+05      3.000000      2.250000   1910.000000  7.618000e+03   \n",
+       "75%    6.450000e+05      4.000000      2.500000   2550.000000  1.068500e+04   \n",
+       "max    7.700000e+06     33.000000      8.000000  13540.000000  1.651359e+06   \n",
+       "\n",
+       "             floors    sqft_above      yr_built  yr_renovated       zipcode  \\\n",
+       "count  21597.000000  21597.000000  21597.000000  17755.000000  21597.000000   \n",
+       "mean       1.494096   1788.596842   1970.999676     83.636778  98077.951845   \n",
+       "std        0.539683    827.759761     29.375234    399.946414     53.513072   \n",
+       "min        1.000000    370.000000   1900.000000      0.000000  98001.000000   \n",
+       "25%        1.000000   1190.000000   1951.000000      0.000000  98033.000000   \n",
+       "50%        1.500000   1560.000000   1975.000000      0.000000  98065.000000   \n",
+       "75%        2.000000   2210.000000   1997.000000      0.000000  98118.000000   \n",
+       "max        3.500000   9410.000000   2015.000000   2015.000000  98199.000000   \n",
+       "\n",
+       "                lat          long  sqft_living15     sqft_lot15  \n",
+       "count  21597.000000  21597.000000   21597.000000   21597.000000  \n",
+       "mean      47.560093   -122.213982    1986.620318   12758.283512  \n",
+       "std        0.138552      0.140724     685.230472   27274.441950  \n",
+       "min       47.155900   -122.519000     399.000000     651.000000  \n",
+       "25%       47.471100   -122.328000    1490.000000    5100.000000  \n",
+       "50%       47.571800   -122.231000    1840.000000    7620.000000  \n",
+       "75%       47.678000   -122.125000    2360.000000   10083.000000  \n",
+       "max       47.777600   -121.315000    6210.000000  871200.000000  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Describing the data\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 21597 entries, 7129300520 to 1523300157\n",
+      "Data columns (total 20 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   date           21597 non-null  object \n",
+      " 1   price          21597 non-null  float64\n",
+      " 2   bedrooms       21597 non-null  int64  \n",
+      " 3   bathrooms      21597 non-null  float64\n",
+      " 4   sqft_living    21597 non-null  int64  \n",
+      " 5   sqft_lot       21597 non-null  int64  \n",
+      " 6   floors         21597 non-null  float64\n",
+      " 7   waterfront     19221 non-null  object \n",
+      " 8   view           21534 non-null  object \n",
+      " 9   condition      21597 non-null  object \n",
+      " 10  grade          21597 non-null  object \n",
+      " 11  sqft_above     21597 non-null  int64  \n",
+      " 12  sqft_basement  21597 non-null  object \n",
+      " 13  yr_built       21597 non-null  int64  \n",
+      " 14  yr_renovated   17755 non-null  float64\n",
+      " 15  zipcode        21597 non-null  int64  \n",
+      " 16  lat            21597 non-null  float64\n",
+      " 17  long           21597 non-null  float64\n",
+      " 18  sqft_living15  21597 non-null  int64  \n",
+      " 19  sqft_lot15     21597 non-null  int64  \n",
+      "dtypes: float64(6), int64(8), object(6)\n",
+      "memory usage: 3.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# summary of the data\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataset contains 21,597 entries and 20 columns\n",
+    "\n",
+    "Some columns like 'waterfront', 'view', 'yr_renovated' have missing values\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### loading the column.md dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('data/column_names.md', 'r') as file:\n",
+    "    md_lines = file.readlines()\n",
+    "\n",
+    "df1 = pd.DataFrame({'Text': md_lines})\n",
+    "\n",
+    "pd.set_option('display.max_colwidth',None)\n",
+    "\n",
+    "# df1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### cleaning the column_md dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1[['Column name', 'Descriptions']] = df1['Text'].str.split('-', n=1,expand=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### drop the original 'text' column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1.drop(columns=['Text'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### remove rows where 'descriptions' columns contains 'None'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Column name</th>\n",
+       "      <th>Descriptions</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>* `id`</td>\n",
+       "      <td>Unique identifier for a house\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>* `date`</td>\n",
+       "      <td>Date house was sold\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>* `price`</td>\n",
+       "      <td>Sale price (prediction target)\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>* `bedrooms`</td>\n",
+       "      <td>Number of bedrooms\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>* `bathrooms`</td>\n",
+       "      <td>Number of bathrooms\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>* `sqft_living`</td>\n",
+       "      <td>Square footage of living space in the home\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>* `sqft_lot`</td>\n",
+       "      <td>Square footage of the lot\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>* `floors`</td>\n",
+       "      <td>Number of floors (levels) in house\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>* `waterfront`</td>\n",
+       "      <td>Whether the house is on a waterfront\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>* `view`</td>\n",
+       "      <td>Quality of view from house\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>* `condition`</td>\n",
+       "      <td>How good the overall condition of the house is. Related to maintenance of house.\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>* `grade`</td>\n",
+       "      <td>Overall grade of the house. Related to the construction and design of the house.\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>* `sqft_above`</td>\n",
+       "      <td>Square footage of house apart from basement\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>* `sqft_basement`</td>\n",
+       "      <td>Square footage of the basement\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>* `yr_built`</td>\n",
+       "      <td>Year when house was built\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>* `yr_renovated`</td>\n",
+       "      <td>Year when house was renovated\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>* `zipcode`</td>\n",
+       "      <td>ZIP Code used by the United States Postal Service\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>* `lat`</td>\n",
+       "      <td>Latitude coordinate\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>* `long`</td>\n",
+       "      <td>Longitude coordinate\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>* `sqft_living15`</td>\n",
+       "      <td>The square footage of interior housing living space for the nearest 15 neighbors\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>* `sqft_lot15`</td>\n",
+       "      <td>The square footage of the land lots of the nearest 15 neighbors\\n</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Column name  \\\n",
+       "1              * `id`    \n",
+       "2            * `date`    \n",
+       "3           * `price`    \n",
+       "4        * `bedrooms`    \n",
+       "5       * `bathrooms`    \n",
+       "6     * `sqft_living`    \n",
+       "7        * `sqft_lot`    \n",
+       "8          * `floors`    \n",
+       "9      * `waterfront`    \n",
+       "11           * `view`    \n",
+       "13      * `condition`    \n",
+       "15          * `grade`    \n",
+       "17     * `sqft_above`    \n",
+       "18  * `sqft_basement`    \n",
+       "19       * `yr_built`    \n",
+       "20   * `yr_renovated`    \n",
+       "21        * `zipcode`    \n",
+       "22            * `lat`    \n",
+       "23           * `long`    \n",
+       "24  * `sqft_living15`    \n",
+       "25     * `sqft_lot15`    \n",
+       "\n",
+       "                                                                           Descriptions  \n",
+       "1                                                       Unique identifier for a house\\n  \n",
+       "2                                                                 Date house was sold\\n  \n",
+       "3                                                      Sale price (prediction target)\\n  \n",
+       "4                                                                  Number of bedrooms\\n  \n",
+       "5                                                                 Number of bathrooms\\n  \n",
+       "6                                          Square footage of living space in the home\\n  \n",
+       "7                                                           Square footage of the lot\\n  \n",
+       "8                                                  Number of floors (levels) in house\\n  \n",
+       "9                                                Whether the house is on a waterfront\\n  \n",
+       "11                                                         Quality of view from house\\n  \n",
+       "13   How good the overall condition of the house is. Related to maintenance of house.\\n  \n",
+       "15   Overall grade of the house. Related to the construction and design of the house.\\n  \n",
+       "17                                        Square footage of house apart from basement\\n  \n",
+       "18                                                     Square footage of the basement\\n  \n",
+       "19                                                          Year when house was built\\n  \n",
+       "20                                                      Year when house was renovated\\n  \n",
+       "21                                  ZIP Code used by the United States Postal Service\\n  \n",
+       "22                                                                Latitude coordinate\\n  \n",
+       "23                                                               Longitude coordinate\\n  \n",
+       "24   The square footage of interior housing living space for the nearest 15 neighbors\\n  \n",
+       "25                    The square footage of the land lots of the nearest 15 neighbors\\n  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = df1[df1['Descriptions'].notna()]\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot',\n",
+       "       'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above',\n",
+       "       'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',\n",
+       "       'sqft_living15', 'sqft_lot15'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking column names\n",
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date                0\n",
+       "price               0\n",
+       "bedrooms            0\n",
+       "bathrooms           0\n",
+       "sqft_living         0\n",
+       "sqft_lot            0\n",
+       "floors              0\n",
+       "waterfront       2376\n",
+       "view               63\n",
+       "condition           0\n",
+       "grade               0\n",
+       "sqft_above          0\n",
+       "sqft_basement       0\n",
+       "yr_built            0\n",
+       "yr_renovated     3842\n",
+       "zipcode             0\n",
+       "lat                 0\n",
+       "long                0\n",
+       "sqft_living15       0\n",
+       "sqft_lot15          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# function to check null values\n",
+    "def check_null(df):\n",
+    "    return df.isna().sum()\n",
+    "\n",
+    "# checking for null values in the data\n",
+    "check_null(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are missing values in three columns.\n",
+    "\n",
+    "Depending on the ratio of missing values, we will decide on what approach to take in dealing with them"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dealing with missing values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There is 11.00152798999861 percent of values missing in waterfront.\n",
+      "There is 0.29170718155299347 percent of values missing in view.\n",
+      "There is 17.78950780200954 percent of values missing in yr_renovated.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# function to calculate percentage of null values\n",
+    "def miss_percent(df,col):\n",
+    "    miss = ((df[col].isna().sum()) / len(df[col])) * 100\n",
+    "    return print(f'There is {miss} percent of values missing in {col}.')\n",
+    "\n",
+    "# checking percentage of missing values \n",
+    "miss_percent(df,'waterfront')\n",
+    "miss_percent(df, 'view')\n",
+    "miss_percent(df, 'yr_renovated')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The threshold on how to deal with missing values commonly used is 50% and also depends on the specific column. The percentages of missing values are very low for the specific columns so we can replace.\n",
+    "\n",
+    "Checking the year renovated column we may assume the missing value is because the house was never renovated, maybe the house did not have a view or a waterfront also for the other two columns hence we can Fill them with zeros.\n",
+    "\n",
+    "Since the missing values in the 3 columns are categorical and are a small percentage of the columns, replacing them with mode won`t skew the data nor give false conclusions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There is 11.00% of values missing in waterfront.\n",
+      "There is 0.29% of values missing in view.\n",
+      "There is 17.79% of values missing in yr_renovated.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def miss_percent(df, col, fill_value=None):\n",
+    "    miss = ((df[col].isna().sum()) / len(df[col])) * 100\n",
+    "    if fill_value is not None:\n",
+    "        df[col].fillna(fill_value, inplace=True)\n",
+    "    return miss\n",
+    "\n",
+    "# checking percentage of missing values and filling missing values with the mode\n",
+    "fill_values = {'waterfront': df['waterfront'].mode()[0], \n",
+    "               'view': df['view'].mode()[0], \n",
+    "               'yr_renovated': df['yr_renovated'].mode()[0]}\n",
+    "\n",
+    "for col in fill_values:\n",
+    "    missing_percent = miss_percent(df, col, fill_value=fill_values[col])\n",
+    "    print(f'There is {missing_percent:.2f}% of values missing in {col}.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### check duplicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duplicates = df.duplicated().sum()\n",
+    "duplicates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### EDA 1\n",
+    "\n",
+    "Exploratory data analysis, including visualizations like scatter plots, bar plots, and heatmaps, is essential before creating regression models. These analyses help understand variable relationships, identify influential predictors, validate model assumptions, and guide feature selection. Visual exploration ensures that subsequent regression models are well-informed, optimized, and interpretable. It also aids in interpreting results and effectively communicating insights, enhancing the overall quality and utility of regression analysis. Conducting thorough exploratory analysis before modeling is crucial for building reliable and actionable regression models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***1. Heatmap***\n",
+    "\n",
+    "The heatmap of correlations between price and features (bedrooms, bathrooms, sqft_living, sqft_lot) is essential for both linear and multilinear regression. It identifies influential predictors based on their relationships with the target variable (price). This helps prioritize predictors in linear models and detect multicollinearity in multilinear models, ensuring stable and interpretable models. Overall, the heatmap guides feature selection and model interpretation in regression analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.heatmap(df[['price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot']].corr(), annot=True, cmap='coolwarm')\n",
+    "plt.title('Correlation Heatmap')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***2. Bar Graph***\n",
+    "\n",
+    "The bar plot of price by condition is essential for developing regression models. It identifies influential condition categories for predictor selection and aids in understanding price variations. This visualization guides preprocessing of categorical variables and validates predictor-target relationships. Overall, it informs feature selection, interpretation, and validation in regression modeling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(x='condition', y='price', data=df, color='blue')\n",
+    "plt.title('Price Distribution by Condition')\n",
+    "plt.xlabel('Condition')\n",
+    "plt.ylabel('Price')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***3. Scatter plot***\n",
+    "\n",
+    "The scatter plots of price against square footage of living space (`sqft_living`) and lot size (`sqft_lot`) provide insights for linear and multilinear regression models. They show how price relates to these predictors, helping assess linearity and identify outliers. Clear trends in these plots guide decisions on model complexity and feature engineering, essential for accurate regression analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.scatterplot(x='sqft_living', y='price', data=df)\n",
+    "plt.title('Price vs Sqft Living')\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.scatterplot(x='sqft_lot', y='price', data=df)\n",
+    "plt.title('Price vs Sqft Lot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PRE-PROCESSING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 21597 entries, 7129300520 to 1523300157\n",
+      "Data columns (total 20 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   date           21597 non-null  object \n",
+      " 1   price          21597 non-null  float64\n",
+      " 2   bedrooms       21597 non-null  int64  \n",
+      " 3   bathrooms      21597 non-null  float64\n",
+      " 4   sqft_living    21597 non-null  int64  \n",
+      " 5   sqft_lot       21597 non-null  int64  \n",
+      " 6   floors         21597 non-null  float64\n",
+      " 7   waterfront     21597 non-null  object \n",
+      " 8   view           21597 non-null  object \n",
+      " 9   condition      21597 non-null  object \n",
+      " 10  grade          21597 non-null  object \n",
+      " 11  sqft_above     21597 non-null  int64  \n",
+      " 12  sqft_basement  21597 non-null  object \n",
+      " 13  yr_built       21597 non-null  int64  \n",
+      " 14  yr_renovated   21597 non-null  float64\n",
+      " 15  zipcode        21597 non-null  int64  \n",
+      " 16  lat            21597 non-null  float64\n",
+      " 17  long           21597 non-null  float64\n",
+      " 18  sqft_living15  21597 non-null  int64  \n",
+      " 19  sqft_lot15     21597 non-null  int64  \n",
+      "dtypes: float64(6), int64(8), object(6)\n",
+      "memory usage: 3.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>2014-10-13</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>2015-02-25</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>2015-02-18</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263000018</th>\n",
+       "      <td>2014-05-21</td>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6600060120</th>\n",
+       "      <td>2015-02-23</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300141</th>\n",
+       "      <td>2014-06-23</td>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291310100</th>\n",
+       "      <td>2015-01-16</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300157</th>\n",
+       "      <td>2014-10-15</td>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+       "id                                                                            \n",
+       "7129300520 2014-10-13  221900.0         3       1.00         1180      5650   \n",
+       "6414100192 2014-12-09  538000.0         3       2.25         2570      7242   \n",
+       "5631500400 2015-02-25  180000.0         2       1.00          770     10000   \n",
+       "2487200875 2014-12-09  604000.0         4       3.00         1960      5000   \n",
+       "1954400510 2015-02-18  510000.0         3       2.00         1680      8080   \n",
+       "...               ...       ...       ...        ...          ...       ...   \n",
+       "263000018  2014-05-21  360000.0         3       2.50         1530      1131   \n",
+       "6600060120 2015-02-23  400000.0         4       2.50         2310      5813   \n",
+       "1523300141 2014-06-23  402101.0         2       0.75         1020      1350   \n",
+       "291310100  2015-01-16  400000.0         3       2.50         1600      2388   \n",
+       "1523300157 2014-10-15  325000.0         2       0.75         1020      1076   \n",
+       "\n",
+       "            floors waterfront  view  condition          grade  sqft_above  \\\n",
+       "id                                                                          \n",
+       "7129300520     1.0         NO  NONE    Average      7 Average        1180   \n",
+       "6414100192     2.0         NO  NONE    Average      7 Average        2170   \n",
+       "5631500400     1.0         NO  NONE    Average  6 Low Average         770   \n",
+       "2487200875     1.0         NO  NONE  Very Good      7 Average        1050   \n",
+       "1954400510     1.0         NO  NONE    Average         8 Good        1680   \n",
+       "...            ...        ...   ...        ...            ...         ...   \n",
+       "263000018      3.0         NO  NONE    Average         8 Good        1530   \n",
+       "6600060120     2.0         NO  NONE    Average         8 Good        2310   \n",
+       "1523300141     2.0         NO  NONE    Average      7 Average        1020   \n",
+       "291310100      2.0         NO  NONE    Average         8 Good        1600   \n",
+       "1523300157     2.0         NO  NONE    Average      7 Average        1020   \n",
+       "\n",
+       "           sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
+       "id                                                                            \n",
+       "7129300520           0.0      1955           0.0    98178  47.5112 -122.257   \n",
+       "6414100192         400.0      1951        1991.0    98125  47.7210 -122.319   \n",
+       "5631500400           0.0      1933           0.0    98028  47.7379 -122.233   \n",
+       "2487200875         910.0      1965           0.0    98136  47.5208 -122.393   \n",
+       "1954400510           0.0      1987           0.0    98074  47.6168 -122.045   \n",
+       "...                  ...       ...           ...      ...      ...      ...   \n",
+       "263000018            0.0      2009           0.0    98103  47.6993 -122.346   \n",
+       "6600060120           0.0      2014           0.0    98146  47.5107 -122.362   \n",
+       "1523300141           0.0      2009           0.0    98144  47.5944 -122.299   \n",
+       "291310100            0.0      2004           0.0    98027  47.5345 -122.069   \n",
+       "1523300157           0.0      2008           0.0    98144  47.5941 -122.299   \n",
+       "\n",
+       "            sqft_living15  sqft_lot15  \n",
+       "id                                     \n",
+       "7129300520           1340        5650  \n",
+       "6414100192           1690        7639  \n",
+       "5631500400           2720        8062  \n",
+       "2487200875           1360        5000  \n",
+       "1954400510           1800        7503  \n",
+       "...                   ...         ...  \n",
+       "263000018            1530        1509  \n",
+       "6600060120           1830        7200  \n",
+       "1523300141           1020        2007  \n",
+       "291310100            1410        1287  \n",
+       "1523300157           1020        1357  \n",
+       "\n",
+       "[21597 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#  Converting date to datetime format \n",
+    "df['date'] = pd.to_datetime(df['date'])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>2014-10-13</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>2015-02-25</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>2015-02-18</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263000018</th>\n",
+       "      <td>2014-05-21</td>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6600060120</th>\n",
+       "      <td>2015-02-23</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300141</th>\n",
+       "      <td>2014-06-23</td>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291310100</th>\n",
+       "      <td>2015-01-16</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300157</th>\n",
+       "      <td>2014-10-15</td>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+       "id                                                                            \n",
+       "7129300520 2014-10-13  221900.0         3       1.00         1180      5650   \n",
+       "6414100192 2014-12-09  538000.0         3       2.25         2570      7242   \n",
+       "5631500400 2015-02-25  180000.0         2       1.00          770     10000   \n",
+       "2487200875 2014-12-09  604000.0         4       3.00         1960      5000   \n",
+       "1954400510 2015-02-18  510000.0         3       2.00         1680      8080   \n",
+       "...               ...       ...       ...        ...          ...       ...   \n",
+       "263000018  2014-05-21  360000.0         3       2.50         1530      1131   \n",
+       "6600060120 2015-02-23  400000.0         4       2.50         2310      5813   \n",
+       "1523300141 2014-06-23  402101.0         2       0.75         1020      1350   \n",
+       "291310100  2015-01-16  400000.0         3       2.50         1600      2388   \n",
+       "1523300157 2014-10-15  325000.0         2       0.75         1020      1076   \n",
+       "\n",
+       "            floors waterfront  view  condition          grade  sqft_above  \\\n",
+       "id                                                                          \n",
+       "7129300520     1.0         NO  NONE    Average      7 Average        1180   \n",
+       "6414100192     2.0         NO  NONE    Average      7 Average        2170   \n",
+       "5631500400     1.0         NO  NONE    Average  6 Low Average         770   \n",
+       "2487200875     1.0         NO  NONE  Very Good      7 Average        1050   \n",
+       "1954400510     1.0         NO  NONE    Average         8 Good        1680   \n",
+       "...            ...        ...   ...        ...            ...         ...   \n",
+       "263000018      3.0         NO  NONE    Average         8 Good        1530   \n",
+       "6600060120     2.0         NO  NONE    Average         8 Good        2310   \n",
+       "1523300141     2.0         NO  NONE    Average      7 Average        1020   \n",
+       "291310100      2.0         NO  NONE    Average         8 Good        1600   \n",
+       "1523300157     2.0         NO  NONE    Average      7 Average        1020   \n",
+       "\n",
+       "           sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
+       "id                                                                            \n",
+       "7129300520           0.0      1955           0.0    98178  47.5112 -122.257   \n",
+       "6414100192         400.0      1951           1.0    98125  47.7210 -122.319   \n",
+       "5631500400           0.0      1933           0.0    98028  47.7379 -122.233   \n",
+       "2487200875         910.0      1965           0.0    98136  47.5208 -122.393   \n",
+       "1954400510           0.0      1987           0.0    98074  47.6168 -122.045   \n",
+       "...                  ...       ...           ...      ...      ...      ...   \n",
+       "263000018            0.0      2009           0.0    98103  47.6993 -122.346   \n",
+       "6600060120           0.0      2014           0.0    98146  47.5107 -122.362   \n",
+       "1523300141           0.0      2009           0.0    98144  47.5944 -122.299   \n",
+       "291310100            0.0      2004           0.0    98027  47.5345 -122.069   \n",
+       "1523300157           0.0      2008           0.0    98144  47.5941 -122.299   \n",
+       "\n",
+       "            sqft_living15  sqft_lot15  \n",
+       "id                                     \n",
+       "7129300520           1340        5650  \n",
+       "6414100192           1690        7639  \n",
+       "5631500400           2720        8062  \n",
+       "2487200875           1360        5000  \n",
+       "1954400510           1800        7503  \n",
+       "...                   ...         ...  \n",
+       "263000018            1530        1509  \n",
+       "6600060120           1830        7200  \n",
+       "1523300141           1020        2007  \n",
+       "291310100            1410        1287  \n",
+       "1523300157           1020        1357  \n",
+       "\n",
+       "[21597 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert any houses that have been renovated to '1' to indicate true and any houses that have not been renovated to '0' to indicate false.\n",
+    "df['yr_renovated'] = df['yr_renovated'].apply(lambda x: 1 if x > 0 else x)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 21597 entries, 7129300520 to 1523300157\n",
+      "Data columns (total 20 columns):\n",
+      " #   Column         Non-Null Count  Dtype         \n",
+      "---  ------         --------------  -----         \n",
+      " 0   date           21597 non-null  datetime64[ns]\n",
+      " 1   price          21597 non-null  float64       \n",
+      " 2   bedrooms       21597 non-null  int64         \n",
+      " 3   bathrooms      21597 non-null  float64       \n",
+      " 4   sqft_living    21597 non-null  int64         \n",
+      " 5   sqft_lot       21597 non-null  int64         \n",
+      " 6   floors         21597 non-null  float64       \n",
+      " 7   waterfront     21597 non-null  object        \n",
+      " 8   view           21597 non-null  object        \n",
+      " 9   condition      21597 non-null  object        \n",
+      " 10  grade          21597 non-null  object        \n",
+      " 11  sqft_above     21597 non-null  int64         \n",
+      " 12  sqft_basement  21597 non-null  object        \n",
+      " 13  yr_built       21597 non-null  int64         \n",
+      " 14  yr_renovated   21597 non-null  float64       \n",
+      " 15  zipcode        21597 non-null  int64         \n",
+      " 16  lat            21597 non-null  float64       \n",
+      " 17  long           21597 non-null  float64       \n",
+      " 18  sqft_living15  21597 non-null  int64         \n",
+      " 19  sqft_lot15     21597 non-null  int64         \n",
+      "dtypes: datetime64[ns](1), float64(6), int64(8), object(5)\n",
+      "memory usage: 3.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['0.0', '400.0', '910.0', '1530.0', '?', '730.0', '1700.0', '300.0',\n",
+       "       '970.0', '760.0', '720.0', '700.0', '820.0', '780.0', '790.0',\n",
+       "       '330.0', '1620.0', '360.0', '588.0', '1510.0', '410.0', '990.0',\n",
+       "       '600.0', '560.0', '550.0', '1000.0', '1600.0', '500.0', '1040.0',\n",
+       "       '880.0', '1010.0', '240.0', '265.0', '290.0', '800.0', '540.0',\n",
+       "       '710.0', '840.0', '380.0', '770.0', '480.0', '570.0', '1490.0',\n",
+       "       '620.0', '1250.0', '1270.0', '120.0', '650.0', '180.0', '1130.0',\n",
+       "       '450.0', '1640.0', '1460.0', '1020.0', '1030.0', '750.0', '640.0',\n",
+       "       '1070.0', '490.0', '1310.0', '630.0', '2000.0', '390.0', '430.0',\n",
+       "       '850.0', '210.0', '1430.0', '1950.0', '440.0', '220.0', '1160.0',\n",
+       "       '860.0', '580.0', '2060.0', '1820.0', '1180.0', '200.0', '1150.0',\n",
+       "       '1200.0', '680.0', '530.0', '1450.0', '1170.0', '1080.0', '960.0',\n",
+       "       '280.0', '870.0', '1100.0', '460.0', '1400.0', '660.0', '1220.0',\n",
+       "       '900.0', '420.0', '1580.0', '1380.0', '475.0', '690.0', '270.0',\n",
+       "       '350.0', '935.0', '1370.0', '980.0', '1470.0', '160.0', '950.0',\n",
+       "       '50.0', '740.0', '1780.0', '1900.0', '340.0', '470.0', '370.0',\n",
+       "       '140.0', '1760.0', '130.0', '520.0', '890.0', '1110.0', '150.0',\n",
+       "       '1720.0', '810.0', '190.0', '1290.0', '670.0', '1800.0', '1120.0',\n",
+       "       '1810.0', '60.0', '1050.0', '940.0', '310.0', '930.0', '1390.0',\n",
+       "       '610.0', '1830.0', '1300.0', '510.0', '1330.0', '1590.0', '920.0',\n",
+       "       '1320.0', '1420.0', '1240.0', '1960.0', '1560.0', '2020.0',\n",
+       "       '1190.0', '2110.0', '1280.0', '250.0', '2390.0', '1230.0', '170.0',\n",
+       "       '830.0', '1260.0', '1410.0', '1340.0', '590.0', '1500.0', '1140.0',\n",
+       "       '260.0', '100.0', '320.0', '1480.0', '1060.0', '1284.0', '1670.0',\n",
+       "       '1350.0', '2570.0', '1090.0', '110.0', '2500.0', '90.0', '1940.0',\n",
+       "       '1550.0', '2350.0', '2490.0', '1481.0', '1360.0', '1135.0',\n",
+       "       '1520.0', '1850.0', '1660.0', '2130.0', '2600.0', '1690.0',\n",
+       "       '243.0', '1210.0', '1024.0', '1798.0', '1610.0', '1440.0',\n",
+       "       '1570.0', '1650.0', '704.0', '1910.0', '1630.0', '2360.0',\n",
+       "       '1852.0', '2090.0', '2400.0', '1790.0', '2150.0', '230.0', '70.0',\n",
+       "       '1680.0', '2100.0', '3000.0', '1870.0', '1710.0', '2030.0',\n",
+       "       '875.0', '1540.0', '2850.0', '2170.0', '506.0', '906.0', '145.0',\n",
+       "       '2040.0', '784.0', '1750.0', '374.0', '518.0', '2720.0', '2730.0',\n",
+       "       '1840.0', '3480.0', '2160.0', '1920.0', '2330.0', '1860.0',\n",
+       "       '2050.0', '4820.0', '1913.0', '80.0', '2010.0', '3260.0', '2200.0',\n",
+       "       '415.0', '1730.0', '652.0', '2196.0', '1930.0', '515.0', '40.0',\n",
+       "       '2080.0', '2580.0', '1548.0', '1740.0', '235.0', '861.0', '1890.0',\n",
+       "       '2220.0', '792.0', '2070.0', '4130.0', '2250.0', '2240.0',\n",
+       "       '1990.0', '768.0', '2550.0', '435.0', '1008.0', '2300.0', '2610.0',\n",
+       "       '666.0', '3500.0', '172.0', '1816.0', '2190.0', '1245.0', '1525.0',\n",
+       "       '1880.0', '862.0', '946.0', '1281.0', '414.0', '2180.0', '276.0',\n",
+       "       '1248.0', '602.0', '516.0', '176.0', '225.0', '1275.0', '266.0',\n",
+       "       '283.0', '65.0', '2310.0', '10.0', '1770.0', '2120.0', '295.0',\n",
+       "       '207.0', '915.0', '556.0', '417.0', '143.0', '508.0', '2810.0',\n",
+       "       '20.0', '274.0', '248.0'], dtype=object)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['sqft_basement'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "sqft_basement has a '?' value, let's replace it with a 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>2014-10-13</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>2015-02-25</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>2015-02-18</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263000018</th>\n",
+       "      <td>2014-05-21</td>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6600060120</th>\n",
+       "      <td>2015-02-23</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300141</th>\n",
+       "      <td>2014-06-23</td>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291310100</th>\n",
+       "      <td>2015-01-16</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300157</th>\n",
+       "      <td>2014-10-15</td>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+       "id                                                                            \n",
+       "7129300520 2014-10-13  221900.0         3       1.00         1180      5650   \n",
+       "6414100192 2014-12-09  538000.0         3       2.25         2570      7242   \n",
+       "5631500400 2015-02-25  180000.0         2       1.00          770     10000   \n",
+       "2487200875 2014-12-09  604000.0         4       3.00         1960      5000   \n",
+       "1954400510 2015-02-18  510000.0         3       2.00         1680      8080   \n",
+       "...               ...       ...       ...        ...          ...       ...   \n",
+       "263000018  2014-05-21  360000.0         3       2.50         1530      1131   \n",
+       "6600060120 2015-02-23  400000.0         4       2.50         2310      5813   \n",
+       "1523300141 2014-06-23  402101.0         2       0.75         1020      1350   \n",
+       "291310100  2015-01-16  400000.0         3       2.50         1600      2388   \n",
+       "1523300157 2014-10-15  325000.0         2       0.75         1020      1076   \n",
+       "\n",
+       "            floors waterfront  view  condition          grade  sqft_above  \\\n",
+       "id                                                                          \n",
+       "7129300520     1.0         NO  NONE    Average      7 Average        1180   \n",
+       "6414100192     2.0         NO  NONE    Average      7 Average        2170   \n",
+       "5631500400     1.0         NO  NONE    Average  6 Low Average         770   \n",
+       "2487200875     1.0         NO  NONE  Very Good      7 Average        1050   \n",
+       "1954400510     1.0         NO  NONE    Average         8 Good        1680   \n",
+       "...            ...        ...   ...        ...            ...         ...   \n",
+       "263000018      3.0         NO  NONE    Average         8 Good        1530   \n",
+       "6600060120     2.0         NO  NONE    Average         8 Good        2310   \n",
+       "1523300141     2.0         NO  NONE    Average      7 Average        1020   \n",
+       "291310100      2.0         NO  NONE    Average         8 Good        1600   \n",
+       "1523300157     2.0         NO  NONE    Average      7 Average        1020   \n",
+       "\n",
+       "            sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
+       "id                                                                             \n",
+       "7129300520            0.0      1955           0.0    98178  47.5112 -122.257   \n",
+       "6414100192          400.0      1951           1.0    98125  47.7210 -122.319   \n",
+       "5631500400            0.0      1933           0.0    98028  47.7379 -122.233   \n",
+       "2487200875          910.0      1965           0.0    98136  47.5208 -122.393   \n",
+       "1954400510            0.0      1987           0.0    98074  47.6168 -122.045   \n",
+       "...                   ...       ...           ...      ...      ...      ...   \n",
+       "263000018             0.0      2009           0.0    98103  47.6993 -122.346   \n",
+       "6600060120            0.0      2014           0.0    98146  47.5107 -122.362   \n",
+       "1523300141            0.0      2009           0.0    98144  47.5944 -122.299   \n",
+       "291310100             0.0      2004           0.0    98027  47.5345 -122.069   \n",
+       "1523300157            0.0      2008           0.0    98144  47.5941 -122.299   \n",
+       "\n",
+       "            sqft_living15  sqft_lot15  \n",
+       "id                                     \n",
+       "7129300520           1340        5650  \n",
+       "6414100192           1690        7639  \n",
+       "5631500400           2720        8062  \n",
+       "2487200875           1360        5000  \n",
+       "1954400510           1800        7503  \n",
+       "...                   ...         ...  \n",
+       "263000018            1530        1509  \n",
+       "6600060120           1830        7200  \n",
+       "1523300141           1020        2007  \n",
+       "291310100            1410        1287  \n",
+       "1523300157           1020        1357  \n",
+       "\n",
+       "[21597 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert sqft_basement to int and replace ? with 0\n",
+    "df['sqft_basement'] = df['sqft_basement'].replace({'?':np.nan}).astype(float)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>2014-10-13</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>2015-02-25</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>2015-02-18</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263000018</th>\n",
+       "      <td>2014-05-21</td>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6600060120</th>\n",
+       "      <td>2015-02-23</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300141</th>\n",
+       "      <td>2014-06-23</td>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291310100</th>\n",
+       "      <td>2015-01-16</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300157</th>\n",
+       "      <td>2014-10-15</td>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+       "id                                                                            \n",
+       "7129300520 2014-10-13  221900.0         3       1.00         1180      5650   \n",
+       "6414100192 2014-12-09  538000.0         3       2.25         2570      7242   \n",
+       "5631500400 2015-02-25  180000.0         2       1.00          770     10000   \n",
+       "2487200875 2014-12-09  604000.0         4       3.00         1960      5000   \n",
+       "1954400510 2015-02-18  510000.0         3       2.00         1680      8080   \n",
+       "...               ...       ...       ...        ...          ...       ...   \n",
+       "263000018  2014-05-21  360000.0         3       2.50         1530      1131   \n",
+       "6600060120 2015-02-23  400000.0         4       2.50         2310      5813   \n",
+       "1523300141 2014-06-23  402101.0         2       0.75         1020      1350   \n",
+       "291310100  2015-01-16  400000.0         3       2.50         1600      2388   \n",
+       "1523300157 2014-10-15  325000.0         2       0.75         1020      1076   \n",
+       "\n",
+       "            floors waterfront  view  condition  ... sqft_basement  yr_built  \\\n",
+       "id                                              ...                           \n",
+       "7129300520     1.0         NO  NONE    Average  ...           0.0      1955   \n",
+       "6414100192     2.0         NO  NONE    Average  ...         400.0      1951   \n",
+       "5631500400     1.0         NO  NONE    Average  ...           0.0      1933   \n",
+       "2487200875     1.0         NO  NONE  Very Good  ...         910.0      1965   \n",
+       "1954400510     1.0         NO  NONE    Average  ...           0.0      1987   \n",
+       "...            ...        ...   ...        ...  ...           ...       ...   \n",
+       "263000018      3.0         NO  NONE    Average  ...           0.0      2009   \n",
+       "6600060120     2.0         NO  NONE    Average  ...           0.0      2014   \n",
+       "1523300141     2.0         NO  NONE    Average  ...           0.0      2009   \n",
+       "291310100      2.0         NO  NONE    Average  ...           0.0      2004   \n",
+       "1523300157     2.0         NO  NONE    Average  ...           0.0      2008   \n",
+       "\n",
+       "            yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "id                                                                   \n",
+       "7129300520           0.0    98178  47.5112 -122.257           1340   \n",
+       "6414100192           1.0    98125  47.7210 -122.319           1690   \n",
+       "5631500400           0.0    98028  47.7379 -122.233           2720   \n",
+       "2487200875           0.0    98136  47.5208 -122.393           1360   \n",
+       "1954400510           0.0    98074  47.6168 -122.045           1800   \n",
+       "...                  ...      ...      ...      ...            ...   \n",
+       "263000018            0.0    98103  47.6993 -122.346           1530   \n",
+       "6600060120           0.0    98146  47.5107 -122.362           1830   \n",
+       "1523300141           0.0    98144  47.5944 -122.299           1020   \n",
+       "291310100            0.0    98027  47.5345 -122.069           1410   \n",
+       "1523300157           0.0    98144  47.5941 -122.299           1020   \n",
+       "\n",
+       "            sqft_lot15  month_of_date  year_of_date  \n",
+       "id                                                   \n",
+       "7129300520        5650             10          2014  \n",
+       "6414100192        7639             12          2014  \n",
+       "5631500400        8062              2          2015  \n",
+       "2487200875        5000             12          2014  \n",
+       "1954400510        7503              2          2015  \n",
+       "...                ...            ...           ...  \n",
+       "263000018         1509              5          2014  \n",
+       "6600060120        7200              2          2015  \n",
+       "1523300141        2007              6          2014  \n",
+       "291310100         1287              1          2015  \n",
+       "1523300157        1357             10          2014  \n",
+       "\n",
+       "[21597 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Retrieve the month and year from the date column.\n",
+    "df['month_of_date'] = pd.DatetimeIndex(df['date']).month\n",
+    "df['year_of_date'] = pd.DatetimeIndex(df['date']).year\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263000018</th>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6600060120</th>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300141</th>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291310100</th>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300157</th>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
+       "id                                                                         \n",
+       "7129300520  221900.0         3       1.00         1180      5650     1.0   \n",
+       "6414100192  538000.0         3       2.25         2570      7242     2.0   \n",
+       "5631500400  180000.0         2       1.00          770     10000     1.0   \n",
+       "2487200875  604000.0         4       3.00         1960      5000     1.0   \n",
+       "1954400510  510000.0         3       2.00         1680      8080     1.0   \n",
+       "...              ...       ...        ...          ...       ...     ...   \n",
+       "263000018   360000.0         3       2.50         1530      1131     3.0   \n",
+       "6600060120  400000.0         4       2.50         2310      5813     2.0   \n",
+       "1523300141  402101.0         2       0.75         1020      1350     2.0   \n",
+       "291310100   400000.0         3       2.50         1600      2388     2.0   \n",
+       "1523300157  325000.0         2       0.75         1020      1076     2.0   \n",
+       "\n",
+       "           waterfront  view  condition          grade  ...  sqft_basement  \\\n",
+       "id                                                     ...                  \n",
+       "7129300520         NO  NONE    Average      7 Average  ...            0.0   \n",
+       "6414100192         NO  NONE    Average      7 Average  ...          400.0   \n",
+       "5631500400         NO  NONE    Average  6 Low Average  ...            0.0   \n",
+       "2487200875         NO  NONE  Very Good      7 Average  ...          910.0   \n",
+       "1954400510         NO  NONE    Average         8 Good  ...            0.0   \n",
+       "...               ...   ...        ...            ...  ...            ...   \n",
+       "263000018          NO  NONE    Average         8 Good  ...            0.0   \n",
+       "6600060120         NO  NONE    Average         8 Good  ...            0.0   \n",
+       "1523300141         NO  NONE    Average      7 Average  ...            0.0   \n",
+       "291310100          NO  NONE    Average         8 Good  ...            0.0   \n",
+       "1523300157         NO  NONE    Average      7 Average  ...            0.0   \n",
+       "\n",
+       "            yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "id                                                                             \n",
+       "7129300520      1955           0.0    98178  47.5112 -122.257           1340   \n",
+       "6414100192      1951           1.0    98125  47.7210 -122.319           1690   \n",
+       "5631500400      1933           0.0    98028  47.7379 -122.233           2720   \n",
+       "2487200875      1965           0.0    98136  47.5208 -122.393           1360   \n",
+       "1954400510      1987           0.0    98074  47.6168 -122.045           1800   \n",
+       "...              ...           ...      ...      ...      ...            ...   \n",
+       "263000018       2009           0.0    98103  47.6993 -122.346           1530   \n",
+       "6600060120      2014           0.0    98146  47.5107 -122.362           1830   \n",
+       "1523300141      2009           0.0    98144  47.5944 -122.299           1020   \n",
+       "291310100       2004           0.0    98027  47.5345 -122.069           1410   \n",
+       "1523300157      2008           0.0    98144  47.5941 -122.299           1020   \n",
+       "\n",
+       "            sqft_lot15  month_of_date  year_of_date  \n",
+       "id                                                   \n",
+       "7129300520        5650             10          2014  \n",
+       "6414100192        7639             12          2014  \n",
+       "5631500400        8062              2          2015  \n",
+       "2487200875        5000             12          2014  \n",
+       "1954400510        7503              2          2015  \n",
+       "...                ...            ...           ...  \n",
+       "263000018         1509              5          2014  \n",
+       "6600060120        7200              2          2015  \n",
+       "1523300141        2007              6          2014  \n",
+       "291310100         1287              1          2015  \n",
+       "1523300157        1357             10          2014  \n",
+       "\n",
+       "[21597 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Drop the date column.\n",
+    "df.drop(columns=['date'], inplace=True)\n",
+    "df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Average', 'Very Good', 'Good', 'Poor', 'Fair'], dtype=object)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Inspecting the condition column\n",
+    "df['condition'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
+       "id                                                                         \n",
+       "7129300520  221900.0         3       1.00         1180      5650     1.0   \n",
+       "6414100192  538000.0         3       2.25         2570      7242     2.0   \n",
+       "5631500400  180000.0         2       1.00          770     10000     1.0   \n",
+       "2487200875  604000.0         4       3.00         1960      5000     1.0   \n",
+       "1954400510  510000.0         3       2.00         1680      8080     1.0   \n",
+       "\n",
+       "           waterfront  view  condition          grade  ...  sqft_basement  \\\n",
+       "id                                                     ...                  \n",
+       "7129300520         NO  NONE          3      7 Average  ...            0.0   \n",
+       "6414100192         NO  NONE          3      7 Average  ...          400.0   \n",
+       "5631500400         NO  NONE          3  6 Low Average  ...            0.0   \n",
+       "2487200875         NO  NONE          1      7 Average  ...          910.0   \n",
+       "1954400510         NO  NONE          3         8 Good  ...            0.0   \n",
+       "\n",
+       "            yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "id                                                                             \n",
+       "7129300520      1955           0.0    98178  47.5112 -122.257           1340   \n",
+       "6414100192      1951           1.0    98125  47.7210 -122.319           1690   \n",
+       "5631500400      1933           0.0    98028  47.7379 -122.233           2720   \n",
+       "2487200875      1965           0.0    98136  47.5208 -122.393           1360   \n",
+       "1954400510      1987           0.0    98074  47.6168 -122.045           1800   \n",
+       "\n",
+       "            sqft_lot15  month_of_date  year_of_date  \n",
+       "id                                                   \n",
+       "7129300520        5650             10          2014  \n",
+       "6414100192        7639             12          2014  \n",
+       "5631500400        8062              2          2015  \n",
+       "2487200875        5000             12          2014  \n",
+       "1954400510        7503              2          2015  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Mapping conditions with the respective number\n",
+    "# Ratings mapping\n",
+    "ratings_mapping = {\n",
+    "    'Average': 3,\n",
+    "    'Very Good': 1,\n",
+    "    'Good': 2,\n",
+    "    'Poor': 4,\n",
+    "    'Fair': 5\n",
+    "}\n",
+    "\n",
+    "# Replace categorical values with numerical values\n",
+    "df['condition'] = df['condition'].replace(ratings_mapping)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['NO', 'YES'], dtype=object)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Inspecting the waterfront column\n",
+    "df['waterfront'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
+       "id                                                                         \n",
+       "7129300520  221900.0         3       1.00         1180      5650     1.0   \n",
+       "6414100192  538000.0         3       2.25         2570      7242     2.0   \n",
+       "5631500400  180000.0         2       1.00          770     10000     1.0   \n",
+       "2487200875  604000.0         4       3.00         1960      5000     1.0   \n",
+       "1954400510  510000.0         3       2.00         1680      8080     1.0   \n",
+       "\n",
+       "            waterfront  view  condition          grade  ...  sqft_basement  \\\n",
+       "id                                                      ...                  \n",
+       "7129300520           0  NONE          3      7 Average  ...            0.0   \n",
+       "6414100192           0  NONE          3      7 Average  ...          400.0   \n",
+       "5631500400           0  NONE          3  6 Low Average  ...            0.0   \n",
+       "2487200875           0  NONE          1      7 Average  ...          910.0   \n",
+       "1954400510           0  NONE          3         8 Good  ...            0.0   \n",
+       "\n",
+       "            yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "id                                                                             \n",
+       "7129300520      1955           0.0    98178  47.5112 -122.257           1340   \n",
+       "6414100192      1951           1.0    98125  47.7210 -122.319           1690   \n",
+       "5631500400      1933           0.0    98028  47.7379 -122.233           2720   \n",
+       "2487200875      1965           0.0    98136  47.5208 -122.393           1360   \n",
+       "1954400510      1987           0.0    98074  47.6168 -122.045           1800   \n",
+       "\n",
+       "            sqft_lot15  month_of_date  year_of_date  \n",
+       "id                                                   \n",
+       "7129300520        5650             10          2014  \n",
+       "6414100192        7639             12          2014  \n",
+       "5631500400        8062              2          2015  \n",
+       "2487200875        5000             12          2014  \n",
+       "1954400510        7503              2          2015  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Mapping waterfront with the respective number \n",
+    "# Replacing YES with 1 and NO with 0\n",
+    "df['waterfront'] = df['waterfront'].astype(str).replace({'YES': 1, 'NO': 0})\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['NONE', 'GOOD', 'EXCELLENT', 'AVERAGE', 'FAIR'], dtype=object)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Inspecting the view column\n",
+    "df['view'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>...</th>\n",
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+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
+       "id                                                                         \n",
+       "7129300520  221900.0         3       1.00         1180      5650     1.0   \n",
+       "6414100192  538000.0         3       2.25         2570      7242     2.0   \n",
+       "5631500400  180000.0         2       1.00          770     10000     1.0   \n",
+       "2487200875  604000.0         4       3.00         1960      5000     1.0   \n",
+       "1954400510  510000.0         3       2.00         1680      8080     1.0   \n",
+       "\n",
+       "            waterfront  view  condition          grade  ...  sqft_basement  \\\n",
+       "id                                                      ...                  \n",
+       "7129300520           0     0          3      7 Average  ...            0.0   \n",
+       "6414100192           0     0          3      7 Average  ...          400.0   \n",
+       "5631500400           0     0          3  6 Low Average  ...            0.0   \n",
+       "2487200875           0     0          1      7 Average  ...          910.0   \n",
+       "1954400510           0     0          3         8 Good  ...            0.0   \n",
+       "\n",
+       "            yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "id                                                                             \n",
+       "7129300520      1955           0.0    98178  47.5112 -122.257           1340   \n",
+       "6414100192      1951           1.0    98125  47.7210 -122.319           1690   \n",
+       "5631500400      1933           0.0    98028  47.7379 -122.233           2720   \n",
+       "2487200875      1965           0.0    98136  47.5208 -122.393           1360   \n",
+       "1954400510      1987           0.0    98074  47.6168 -122.045           1800   \n",
+       "\n",
+       "            sqft_lot15  month_of_date  year_of_date  \n",
+       "id                                                   \n",
+       "7129300520        5650             10          2014  \n",
+       "6414100192        7639             12          2014  \n",
+       "5631500400        8062              2          2015  \n",
+       "2487200875        5000             12          2014  \n",
+       "1954400510        7503              2          2015  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define the mappings\n",
+    "quality_mapping = {\n",
+    "    'NONE': 0,\n",
+    "    'GOOD': 1,\n",
+    "    'EXCELLENT': 2,\n",
+    "    'AVERAGE': 3,\n",
+    "    'FAIR': 4\n",
+    "}\n",
+    "\n",
+    "# Replace the values using the mapping\n",
+    "df['view'] = df['view'].replace(quality_mapping)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['7 Average', '6 Low Average', '8 Good', '11 Excellent', '9 Better',\n",
+       "       '5 Fair', '10 Very Good', '12 Luxury', '4 Low', '3 Poor',\n",
+       "       '13 Mansion'], dtype=object)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Inspecting the grade column\n",
+    "df['grade'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263000018</th>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6600060120</th>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300141</th>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291310100</th>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300157</th>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
+       "id                                                                         \n",
+       "7129300520  221900.0         3       1.00         1180      5650     1.0   \n",
+       "6414100192  538000.0         3       2.25         2570      7242     2.0   \n",
+       "5631500400  180000.0         2       1.00          770     10000     1.0   \n",
+       "2487200875  604000.0         4       3.00         1960      5000     1.0   \n",
+       "1954400510  510000.0         3       2.00         1680      8080     1.0   \n",
+       "...              ...       ...        ...          ...       ...     ...   \n",
+       "263000018   360000.0         3       2.50         1530      1131     3.0   \n",
+       "6600060120  400000.0         4       2.50         2310      5813     2.0   \n",
+       "1523300141  402101.0         2       0.75         1020      1350     2.0   \n",
+       "291310100   400000.0         3       2.50         1600      2388     2.0   \n",
+       "1523300157  325000.0         2       0.75         1020      1076     2.0   \n",
+       "\n",
+       "            waterfront  view  condition  grade  ...  sqft_basement  yr_built  \\\n",
+       "id                                              ...                            \n",
+       "7129300520           0     0          3      7  ...            0.0      1955   \n",
+       "6414100192           0     0          3      7  ...          400.0      1951   \n",
+       "5631500400           0     0          3      6  ...            0.0      1933   \n",
+       "2487200875           0     0          1      7  ...          910.0      1965   \n",
+       "1954400510           0     0          3      8  ...            0.0      1987   \n",
+       "...                ...   ...        ...    ...  ...            ...       ...   \n",
+       "263000018            0     0          3      8  ...            0.0      2009   \n",
+       "6600060120           0     0          3      8  ...            0.0      2014   \n",
+       "1523300141           0     0          3      7  ...            0.0      2009   \n",
+       "291310100            0     0          3      8  ...            0.0      2004   \n",
+       "1523300157           0     0          3      7  ...            0.0      2008   \n",
+       "\n",
+       "            yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "id                                                                   \n",
+       "7129300520           0.0    98178  47.5112 -122.257           1340   \n",
+       "6414100192           1.0    98125  47.7210 -122.319           1690   \n",
+       "5631500400           0.0    98028  47.7379 -122.233           2720   \n",
+       "2487200875           0.0    98136  47.5208 -122.393           1360   \n",
+       "1954400510           0.0    98074  47.6168 -122.045           1800   \n",
+       "...                  ...      ...      ...      ...            ...   \n",
+       "263000018            0.0    98103  47.6993 -122.346           1530   \n",
+       "6600060120           0.0    98146  47.5107 -122.362           1830   \n",
+       "1523300141           0.0    98144  47.5944 -122.299           1020   \n",
+       "291310100            0.0    98027  47.5345 -122.069           1410   \n",
+       "1523300157           0.0    98144  47.5941 -122.299           1020   \n",
+       "\n",
+       "            sqft_lot15  month_of_date  year_of_date  \n",
+       "id                                                   \n",
+       "7129300520        5650             10          2014  \n",
+       "6414100192        7639             12          2014  \n",
+       "5631500400        8062              2          2015  \n",
+       "2487200875        5000             12          2014  \n",
+       "1954400510        7503              2          2015  \n",
+       "...                ...            ...           ...  \n",
+       "263000018         1509              5          2014  \n",
+       "6600060120        7200              2          2015  \n",
+       "1523300141        2007              6          2014  \n",
+       "291310100         1287              1          2015  \n",
+       "1523300157        1357             10          2014  \n",
+       "\n",
+       "[21597 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define the mappings\n",
+    "rating_mapping = {\n",
+    "    'Average': 7,\n",
+    "    'Low Average': 6,\n",
+    "    'Good': 8,\n",
+    "    'Excellent': 11,\n",
+    "    'Better': 9,\n",
+    "    'Fair': 5,\n",
+    "    'Very Good': 10,\n",
+    "    'Luxury': 12,\n",
+    "    'Low': 4,\n",
+    "    'Poor': 3,\n",
+    "    'Mansion': 13\n",
+    "}\n",
+    "\n",
+    "# Extract the rating string and replace with the corresponding numerical value\n",
+    "df['grade'] = df['grade'].str.extract('(\\d+)').astype(int)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 21597 entries, 7129300520 to 1523300157\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   price          21597 non-null  float64\n",
+      " 1   bedrooms       21597 non-null  int64  \n",
+      " 2   bathrooms      21597 non-null  float64\n",
+      " 3   sqft_living    21597 non-null  int64  \n",
+      " 4   sqft_lot       21597 non-null  int64  \n",
+      " 5   floors         21597 non-null  float64\n",
+      " 6   waterfront     21597 non-null  int64  \n",
+      " 7   view           21597 non-null  int64  \n",
+      " 8   condition      21597 non-null  int64  \n",
+      " 9   grade          21597 non-null  int32  \n",
+      " 10  sqft_above     21597 non-null  int64  \n",
+      " 11  sqft_basement  21143 non-null  float64\n",
+      " 12  yr_built       21597 non-null  int64  \n",
+      " 13  yr_renovated   21597 non-null  float64\n",
+      " 14  zipcode        21597 non-null  int64  \n",
+      " 15  lat            21597 non-null  float64\n",
+      " 16  long           21597 non-null  float64\n",
+      " 17  sqft_living15  21597 non-null  int64  \n",
+      " 18  sqft_lot15     21597 non-null  int64  \n",
+      " 19  month_of_date  21597 non-null  int32  \n",
+      " 20  year_of_date   21597 non-null  int32  \n",
+      "dtypes: float64(7), int32(3), int64(11)\n",
+      "memory usage: 3.4 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sqft_living      0.701917\n",
+       "grade            0.667951\n",
+       "sqft_above       0.605368\n",
+       "sqft_living15    0.585241\n",
+       "bathrooms        0.525906\n",
+       "sqft_basement    0.325008\n",
+       "bedrooms         0.308787\n",
+       "lat              0.306692\n",
+       "view             0.290620\n",
+       "waterfront       0.264306\n",
+       "floors           0.256804\n",
+       "yr_renovated     0.117543\n",
+       "sqft_lot         0.089876\n",
+       "sqft_lot15       0.082845\n",
+       "yr_built         0.053953\n",
+       "zipcode          0.053402\n",
+       "condition        0.040742\n",
+       "long             0.022036\n",
+       "month_of_date    0.009928\n",
+       "year_of_date     0.003727\n",
+       "Name: price, dtype: float64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Checking the correlation between price (target) and predictors\n",
+    "df.corr()['price'].drop(['price']).map(abs).sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "sqft_living, grade, sqft_above have the highest correlation with the target while year_of_date, month_of_date and long have the lowest correlation with the target."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Identfying Numeric and categorical columns\n",
+    "numeric = ['bedrooms', \n",
+    "           'bathrooms', \n",
+    "           'sqft_living', \n",
+    "           'sqft_lot', \n",
+    "           'sqft_above', \n",
+    "           'sqft_basement',\n",
+    "           'lat', \n",
+    "           'long',\n",
+    "           'sqft_living15', \n",
+    "           'sqft_lot15']\n",
+    "\n",
+    "categorical = ['floors',\n",
+    "               'waterfront', \n",
+    "               'view', \n",
+    "               'condition', \n",
+    "               'grade',\n",
+    "               'yr_renovated',\n",
+    "               'zipcode',\n",
+    "               'month_of_date']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21592</th>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21593</th>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21594</th>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21595</th>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21596</th>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
+       "0      221900.0         3       1.00         1180      5650     1.0   \n",
+       "1      538000.0         3       2.25         2570      7242     2.0   \n",
+       "2      180000.0         2       1.00          770     10000     1.0   \n",
+       "3      604000.0         4       3.00         1960      5000     1.0   \n",
+       "4      510000.0         3       2.00         1680      8080     1.0   \n",
+       "...         ...       ...        ...          ...       ...     ...   \n",
+       "21592  360000.0         3       2.50         1530      1131     3.0   \n",
+       "21593  400000.0         4       2.50         2310      5813     2.0   \n",
+       "21594  402101.0         2       0.75         1020      1350     2.0   \n",
+       "21595  400000.0         3       2.50         1600      2388     2.0   \n",
+       "21596  325000.0         2       0.75         1020      1076     2.0   \n",
+       "\n",
+       "       waterfront  view  condition  grade  ...  sqft_basement  yr_built  \\\n",
+       "0               0     0          3      7  ...            0.0      1955   \n",
+       "1               0     0          3      7  ...          400.0      1951   \n",
+       "2               0     0          3      6  ...            0.0      1933   \n",
+       "3               0     0          1      7  ...          910.0      1965   \n",
+       "4               0     0          3      8  ...            0.0      1987   \n",
+       "...           ...   ...        ...    ...  ...            ...       ...   \n",
+       "21592           0     0          3      8  ...            0.0      2009   \n",
+       "21593           0     0          3      8  ...            0.0      2014   \n",
+       "21594           0     0          3      7  ...            0.0      2009   \n",
+       "21595           0     0          3      8  ...            0.0      2004   \n",
+       "21596           0     0          3      7  ...            0.0      2008   \n",
+       "\n",
+       "       yr_renovated  zipcode      lat     long  sqft_living15  sqft_lot15  \\\n",
+       "0               0.0    98178  47.5112 -122.257           1340        5650   \n",
+       "1               1.0    98125  47.7210 -122.319           1690        7639   \n",
+       "2               0.0    98028  47.7379 -122.233           2720        8062   \n",
+       "3               0.0    98136  47.5208 -122.393           1360        5000   \n",
+       "4               0.0    98074  47.6168 -122.045           1800        7503   \n",
+       "...             ...      ...      ...      ...            ...         ...   \n",
+       "21592           0.0    98103  47.6993 -122.346           1530        1509   \n",
+       "21593           0.0    98146  47.5107 -122.362           1830        7200   \n",
+       "21594           0.0    98144  47.5944 -122.299           1020        2007   \n",
+       "21595           0.0    98027  47.5345 -122.069           1410        1287   \n",
+       "21596           0.0    98144  47.5941 -122.299           1020        1357   \n",
+       "\n",
+       "       month_of_date  year_of_date  \n",
+       "0                 10          2014  \n",
+       "1                 12          2014  \n",
+       "2                  2          2015  \n",
+       "3                 12          2014  \n",
+       "4                  2          2015  \n",
+       "...              ...           ...  \n",
+       "21592              5          2014  \n",
+       "21593              2          2015  \n",
+       "21594              6          2014  \n",
+       "21595              1          2015  \n",
+       "21596             10          2014  \n",
+       "\n",
+       "[21597 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#reset index and dropping the id column\n",
+    "df.reset_index(inplace=True, drop=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 21597 entries, 0 to 21596\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   price          21597 non-null  float64\n",
+      " 1   bedrooms       21597 non-null  int64  \n",
+      " 2   bathrooms      21597 non-null  float64\n",
+      " 3   sqft_living    21597 non-null  int64  \n",
+      " 4   sqft_lot       21597 non-null  int64  \n",
+      " 5   floors         21597 non-null  float64\n",
+      " 6   waterfront     21597 non-null  int64  \n",
+      " 7   view           21597 non-null  int64  \n",
+      " 8   condition      21597 non-null  int64  \n",
+      " 9   grade          21597 non-null  int32  \n",
+      " 10  sqft_above     21597 non-null  int64  \n",
+      " 11  sqft_basement  21143 non-null  float64\n",
+      " 12  yr_built       21597 non-null  int64  \n",
+      " 13  yr_renovated   21597 non-null  float64\n",
+      " 14  zipcode        21597 non-null  int64  \n",
+      " 15  lat            21597 non-null  float64\n",
+      " 16  long           21597 non-null  float64\n",
+      " 17  sqft_living15  21597 non-null  int64  \n",
+      " 18  sqft_lot15     21597 non-null  int64  \n",
+      " 19  month_of_date  21597 non-null  int32  \n",
+      " 20  year_of_date   21597 non-null  int32  \n",
+      "dtypes: float64(7), int32(3), int64(11)\n",
+      "memory usage: 3.2 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "price            float64\n",
+       "bedrooms           int64\n",
+       "bathrooms        float64\n",
+       "sqft_living        int64\n",
+       "sqft_lot           int64\n",
+       "floors           float64\n",
+       "waterfront         int64\n",
+       "view               int64\n",
+       "condition          int64\n",
+       "grade              int32\n",
+       "sqft_above         int64\n",
+       "sqft_basement    float64\n",
+       "yr_built           int64\n",
+       "yr_renovated     float64\n",
+       "zipcode            int64\n",
+       "lat              float64\n",
+       "long             float64\n",
+       "sqft_living15      int64\n",
+       "sqft_lot15         int64\n",
+       "month_of_date      int32\n",
+       "year_of_date       int32\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2.159700e+04</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>2.159700e+04</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>21143.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.402966e+05</td>\n",
+       "      <td>3.373200</td>\n",
+       "      <td>2.115826</td>\n",
+       "      <td>2080.321850</td>\n",
+       "      <td>1.509941e+04</td>\n",
+       "      <td>1.494096</td>\n",
+       "      <td>0.006760</td>\n",
+       "      <td>0.246932</td>\n",
+       "      <td>2.596703</td>\n",
+       "      <td>7.657915</td>\n",
+       "      <td>...</td>\n",
+       "      <td>291.851724</td>\n",
+       "      <td>1970.999676</td>\n",
+       "      <td>0.034449</td>\n",
+       "      <td>98077.951845</td>\n",
+       "      <td>47.560093</td>\n",
+       "      <td>-122.213982</td>\n",
+       "      <td>1986.620318</td>\n",
+       "      <td>12758.283512</td>\n",
+       "      <td>6.573969</td>\n",
+       "      <td>2014.322962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.673681e+05</td>\n",
+       "      <td>0.926299</td>\n",
+       "      <td>0.768984</td>\n",
+       "      <td>918.106125</td>\n",
+       "      <td>4.141264e+04</td>\n",
+       "      <td>0.539683</td>\n",
+       "      <td>0.081944</td>\n",
+       "      <td>0.815213</td>\n",
+       "      <td>0.669407</td>\n",
+       "      <td>1.173200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>442.498337</td>\n",
+       "      <td>29.375234</td>\n",
+       "      <td>0.182384</td>\n",
+       "      <td>53.513072</td>\n",
+       "      <td>0.138552</td>\n",
+       "      <td>0.140724</td>\n",
+       "      <td>685.230472</td>\n",
+       "      <td>27274.441950</td>\n",
+       "      <td>3.115061</td>\n",
+       "      <td>0.467619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>7.800000e+04</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>5.200000e+02</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1900.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98001.000000</td>\n",
+       "      <td>47.155900</td>\n",
+       "      <td>-122.519000</td>\n",
+       "      <td>399.000000</td>\n",
+       "      <td>651.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2014.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.220000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.750000</td>\n",
+       "      <td>1430.000000</td>\n",
+       "      <td>5.040000e+03</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1951.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98033.000000</td>\n",
+       "      <td>47.471100</td>\n",
+       "      <td>-122.328000</td>\n",
+       "      <td>1490.000000</td>\n",
+       "      <td>5100.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2014.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>4.500000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.250000</td>\n",
+       "      <td>1910.000000</td>\n",
+       "      <td>7.618000e+03</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1975.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98065.000000</td>\n",
+       "      <td>47.571800</td>\n",
+       "      <td>-122.231000</td>\n",
+       "      <td>1840.000000</td>\n",
+       "      <td>7620.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2014.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.450000e+05</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>2550.000000</td>\n",
+       "      <td>1.068500e+04</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>560.000000</td>\n",
+       "      <td>1997.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98118.000000</td>\n",
+       "      <td>47.678000</td>\n",
+       "      <td>-122.125000</td>\n",
+       "      <td>2360.000000</td>\n",
+       "      <td>10083.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.700000e+06</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>13540.000000</td>\n",
+       "      <td>1.651359e+06</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4820.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>98199.000000</td>\n",
+       "      <td>47.777600</td>\n",
+       "      <td>-121.315000</td>\n",
+       "      <td>6210.000000</td>\n",
+       "      <td>871200.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\n",
+       "count  2.159700e+04  21597.000000  21597.000000  21597.000000  2.159700e+04   \n",
+       "mean   5.402966e+05      3.373200      2.115826   2080.321850  1.509941e+04   \n",
+       "std    3.673681e+05      0.926299      0.768984    918.106125  4.141264e+04   \n",
+       "min    7.800000e+04      1.000000      0.500000    370.000000  5.200000e+02   \n",
+       "25%    3.220000e+05      3.000000      1.750000   1430.000000  5.040000e+03   \n",
+       "50%    4.500000e+05      3.000000      2.250000   1910.000000  7.618000e+03   \n",
+       "75%    6.450000e+05      4.000000      2.500000   2550.000000  1.068500e+04   \n",
+       "max    7.700000e+06     33.000000      8.000000  13540.000000  1.651359e+06   \n",
+       "\n",
+       "             floors    waterfront          view     condition         grade  \\\n",
+       "count  21597.000000  21597.000000  21597.000000  21597.000000  21597.000000   \n",
+       "mean       1.494096      0.006760      0.246932      2.596703      7.657915   \n",
+       "std        0.539683      0.081944      0.815213      0.669407      1.173200   \n",
+       "min        1.000000      0.000000      0.000000      1.000000      3.000000   \n",
+       "25%        1.000000      0.000000      0.000000      2.000000      7.000000   \n",
+       "50%        1.500000      0.000000      0.000000      3.000000      7.000000   \n",
+       "75%        2.000000      0.000000      0.000000      3.000000      8.000000   \n",
+       "max        3.500000      1.000000      4.000000      5.000000     13.000000   \n",
+       "\n",
+       "       ...  sqft_basement      yr_built  yr_renovated       zipcode  \\\n",
+       "count  ...   21143.000000  21597.000000  21597.000000  21597.000000   \n",
+       "mean   ...     291.851724   1970.999676      0.034449  98077.951845   \n",
+       "std    ...     442.498337     29.375234      0.182384     53.513072   \n",
+       "min    ...       0.000000   1900.000000      0.000000  98001.000000   \n",
+       "25%    ...       0.000000   1951.000000      0.000000  98033.000000   \n",
+       "50%    ...       0.000000   1975.000000      0.000000  98065.000000   \n",
+       "75%    ...     560.000000   1997.000000      0.000000  98118.000000   \n",
+       "max    ...    4820.000000   2015.000000      1.000000  98199.000000   \n",
+       "\n",
+       "                lat          long  sqft_living15     sqft_lot15  \\\n",
+       "count  21597.000000  21597.000000   21597.000000   21597.000000   \n",
+       "mean      47.560093   -122.213982    1986.620318   12758.283512   \n",
+       "std        0.138552      0.140724     685.230472   27274.441950   \n",
+       "min       47.155900   -122.519000     399.000000     651.000000   \n",
+       "25%       47.471100   -122.328000    1490.000000    5100.000000   \n",
+       "50%       47.571800   -122.231000    1840.000000    7620.000000   \n",
+       "75%       47.678000   -122.125000    2360.000000   10083.000000   \n",
+       "max       47.777600   -121.315000    6210.000000  871200.000000   \n",
+       "\n",
+       "       month_of_date  year_of_date  \n",
+       "count   21597.000000  21597.000000  \n",
+       "mean        6.573969   2014.322962  \n",
+       "std         3.115061      0.467619  \n",
+       "min         1.000000   2014.000000  \n",
+       "25%         4.000000   2014.000000  \n",
+       "50%         6.000000   2014.000000  \n",
+       "75%         9.000000   2015.000000  \n",
+       "max        12.000000   2015.000000  \n",
+       "\n",
+       "[8 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MODELLING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***1. Linear Regression model***\n",
+    "\n",
+    "We will pick **`sqft_living` - Square footage of living space in the home** to be used to create our linear regression model because it has the most correlation with the price and it has the most linear scatter plor hence a good candidate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error: 65977373783.62\n",
+      "R-squared Score: 0.49\n",
+      "Root Mean Squared Error (RMSE): 256860.61158460553\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "X = df[['sqft_living']]\n",
+    "y = df['price']\n",
+    "\n",
+    "# Splitting the dataset into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Creating the linear regression model\n",
+    "model = LinearRegression()\n",
+    "\n",
+    "# Training the model\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Making predictions\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# Evaluating the model\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "rmse = np.sqrt(mse)\n",
+    "\n",
+    "print(f'Mean Squared Error: {mse:.2f}')\n",
+    "print(f'R-squared Score: {r2:.2f}')\n",
+    "print(\"Root Mean Squared Error (RMSE):\", rmse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  price   R-squared:                       0.492\n",
+      "Model:                            OLS   Adj. R-squared:                  0.492\n",
+      "Method:                 Least Squares   F-statistic:                 1.676e+04\n",
+      "Date:                Mon, 29 Apr 2024   Prob (F-statistic):               0.00\n",
+      "Time:                        09:56:25   Log-Likelihood:            -2.4012e+05\n",
+      "No. Observations:               17277   AIC:                         4.802e+05\n",
+      "Df Residuals:                   17275   BIC:                         4.803e+05\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "===============================================================================\n",
+      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "-------------------------------------------------------------------------------\n",
+      "const       -4.645e+04   4961.972     -9.361      0.000   -5.62e+04   -3.67e+04\n",
+      "sqft_living   282.2015      2.180    129.476      0.000     277.929     286.474\n",
+      "==============================================================================\n",
+      "Omnibus:                    11495.535   Durbin-Watson:                   1.996\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           371098.388\n",
+      "Skew:                           2.737   Prob(JB):                         0.00\n",
+      "Kurtosis:                      25.035   Cond. No.                     5.65e+03\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "[2] The condition number is large, 5.65e+03. This might indicate that there are\n",
+      "strong multicollinearity or other numerical problems.\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Add a constant term to the predictor variable for the intercept\n",
+    "X_train_with_const = sm.add_constant(X_train)\n",
+    "\n",
+    "# Fit the linear regression model\n",
+    "model = sm.OLS(y_train, X_train_with_const)\n",
+    "results = model.fit()\n",
+    "\n",
+    "# Display the summary table of regression results\n",
+    "print(results.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This model has an RMSE of 256860.61, indicating significant variability in the model's predictions. The R-squared score is 0.49, indicating that 49% of the variance in house prices can be explained by the square footage alone. While an R-squared value of 0.49 indicates that the model explains a moderate amount of the variability in house prices, it also suggests that there is still a substantial amount of variability that remains unexplained by the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting the regression line\n",
+    "data = pd.concat([X_train, y_train], axis=1)\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.regplot(x='sqft_living', y='price', data=data, scatter_kws={'alpha':0.5}, line_kws={'color':'red', 'linewidth':2})\n",
+    "plt.title('Linear Regression: Price vs Sqft Living')\n",
+    "plt.xlabel('Sqft Living')\n",
+    "plt.ylabel('Price')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Log transformation**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We applied a log transformation on house prices (target variable) to stabilize variance, reduce the impact of outliers and ensure the target variable follows a normal distribution as much as possible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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w77XXXpO7u7tmzpwpNzc3tWnTRocOHdKKFSs0aNAgFRQUaNWqVZo0aZJ69eolSUpISFBkZKQ2b96sAQMGXPUcpktcmy5JGj+kq8WTAAAA1DzLrwx/8803uu666/TWW28pJCTEYd/OnTsVHh4uN7f/NntERIQOHDig7OxsZWZmKjc3VxEREfb9vr6+CgoK0o4dO8p1DtPl2gqVy9VjAABgKMuvDPfp00d9+vQpc9/x48fVrl07h21+fn6SpKNHj+r48eOSpObNm5c65tixY+U6R9OmTa/9RQAAAKBWsvzK8JXYbDa5u7s7bPPw8JAk5efn6+zZs5JU5jH5+fnlOgcAAADM5dQx7OnpaX8jXImSgPX29panp6cklXmMl5dXuc4BAAAAczl1DAcEBCgrK8thW8nX/v7+9uURZR0TEBBQrnMAAADAXE4dw+Hh4dq1a5eKiors27Zu3arWrVuradOmCgwMlI+Pj7Zt22bff/r0aWVkZCgsLKxc5wAAAIC5nDqGBw0apDNnzmjatGnat2+f1q9fr5deekljxoyRdGGtcHR0tOLj47VlyxZlZmZqwoQJCggIUL9+/cp1DgAAAJjL8rtJXEnTpk21cuVKzZkzR1FRUWrWrJkmT56sqKgo+zHjxo1TYWGhpk+fLpvNpvDwcCUlJdnfNFeecwAAAMBMThXD8+bNK7Wtc+fOSklJuexj6tWrp0mTJmnSpEmXPeZq5wAAAICZnHqZBAAAAFCdiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGMvN6gFQvbw83JS4Nl1eHnyrAQAALkUhGSDXVqhiq4cAAABwQiyTAAAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhyNvTTYlr05W4Nt3qUQAAAGqUm9UDwDnk2gqtHgEAAKDGcWUYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoZRCrdZAwAApuDWaiiF26wBAABTcGUYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAs3kBXRyWuTZeXh5tcXFysHgUAAMBpcWW4jsq1FSovn7tCAAAAXAkxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwlpvVA6DmeXm4KXFturw8+PYDAACzUUOGyrUVqtjqIQAAACzGMgkAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYhp23p5te3phh9RgAAAA1hhiGg7P5RVaPAAAAUGOIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsWpFDJ87d04JCQnq3bu3unTpogcffFDp6en2/Xv27FF0dLRCQ0PVu3dvJSUlOTz+/PnzWrhwoSIjIxUSEqJRo0bp0KFDNf0yAAAA4GRqRQwvW7ZMqampmj17ttLS0nTLLbcoJiZGJ06c0KlTpzRy5EjdfPPNSk1NVVxcnBYsWKDU1FT745cuXap169Zp9uzZSklJkYuLi2JiYlRQUGDhqwIAAIDVakUMb9myRXfffbd69uypVq1a6cknn9SZM2e0e/duvfbaa3J3d9fMmTPVpk0bDRo0SCNGjNCKFSskSQUFBVq1apXi4uLUq1cvBQYGKiEhQSdOnNDmzZstfmUAAACwUq2I4UaNGunDDz/U4cOHVVRUpJSUFLm7u6tDhw7auXOnwsPD5ebmZj8+IiJCBw4cUHZ2tjIzM5Wbm6uIiAj7fl9fXwUFBWnHjh1WvBwAAAA4CberH2K9adOmacKECerbt6/q1asnV1dXLViwQC1bttTx48fVrl07h+P9/PwkSUePHtXx48clSc2bNy91zLFjx2rmBQAAAMAp1YoY3r9/v3x9fbVkyRL5+/vr9ddf15QpU7RmzRrZbDa5u7s7HO/h4SFJys/P19mzZyWpzGNycnJq5gUAAADAKTl9DB85ckSTJk1ScnKywsLCJEnBwcHat2+fFi1aJE9Pz1JvhMvPz5ckeXt7y9PTU9KFtcMlPy85xsvLq4ZeBQAAAJyR068Z/vLLL3Xu3DkFBwc7bA8JCdHBgwcVEBCgrKwsh30lX/v7+9uXR5R1TEBAQDVODgAAAGfn9DFcErPffvutw/a9e/eqVatWCg8P165du1RUVGTft3XrVrVu3VpNmzZVYGCgfHx8tG3bNvv+06dPKyMjw36lGQAAAGZy+hju3LmzwsLCNGXKFH322Wc6ePCgEhMTtXXrVj3yyCMaNGiQzpw5o2nTpmnfvn1av369XnrpJY0ZM0bShbXC0dHRio+P15YtW5SZmakJEyYoICBA/fr1s/jVAQAAwEpOv2bY1dVVS5cuVWJioqZOnaqcnBy1a9dOycnJCg0NlSStXLlSc+bMUVRUlJo1a6bJkycrKirKfo5x48apsLBQ06dPl81mU3h4uJKSkkq9qQ4AAABmcSkuLi62eojapm/fvpIufBiIs5qzeru8Pd3UwLO+Tv5yVt6ebsqzFV5226X7JGnayG4WvwoAAIDKKW+vOf0yCQAAAKC6EMMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAY7lZPQCqVuLadHl58G0FAAAoD6qpjsm1FarY6iEAAABqCZZJAAAAwFjEMAAAAIxFDAMAAMBYxDDK5O3ppsS16Upcm271KAAAANWGN9DhsnJthVaPAAAAUK24MgwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxjHLhzhIAAKAu4m4SKBfuLAEAAOoirgwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUM44q8Pd308sYMq8cAAACoFsQwrupsfpGkC2GcuDZdiWvTLZ4IAACgarhZPQBql1xbodUjAAAAVBmuDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYlYrhHTt2KDc3t8x9p0+f1jvvvHNNQwEAAAA1oVIx/NBDD2n//v1l7svIyNDUqVOvaaiypKWlqX///goODtaAAQO0adMm+749e/YoOjpaoaGh6t27t5KSkhwee/78eS1cuFCRkZEKCQnRqFGjdOjQoSqfEQAAALWLW3kPnDJlio4dOyZJKi4u1syZM+Xj41PquIMHD+r666+vugklvfnmm3rqqac0ZcoU9e7dWxs2bNATTzyhgIAA3XzzzRo5cqTuvPNOzZo1S7t379asWbPUqFEjDRo0SJK0dOlSrVu3TnPnzpW/v7/mz5+vmJgYbdiwQe7u7lU6KwAAAGqPcl8Z/t3vfqfi4mIVFxfbt5V8XfLD1dVVoaGhmjt3bpUNWFxcrAULFmj48OEaPny4WrVqpbFjx+q2227T9u3b9dprr8nd3V0zZ85UmzZtNGjQII0YMUIrVqyQJBUUFGjVqlWKi4tTr169FBgYqISEBJ04cUKbN2+usjlN4u3ppsS16Upcm271KAAAANek3FeG+/Tpoz59+kiShg0bZo/P6vb999/ryJEjGjhwoMP2kqUQMTExCg8Pl5vbf19KRESEXnjhBWVnZ+vIkSPKzc1VRESEfb+vr6+CgoK0Y8cODRgwoNpfQ12Uayu0egQAAIBrVqk1wy+//HKNhLB0YdmFJOXl5Wn06NHq0aOHBg8erA8++ECSdPz4cQUEBDg8xs/PT5J09OhRHT9+XJLUvHnzUseULPsAAACAmcp9ZfhiZ8+e1fLly/Xhhx/q7NmzOn/+vMN+FxcXvf/++1Uy4JkzZyRdWLP8+OOPa+LEiXr33XcVGxur1atXy2azlVr36+HhIUnKz8/X2bNnJanMY3JycqpkRgAAANROlYrhOXPmKDU1Vd26dVOHDh3k6lp9tyuuX7++JGn06NGKioqSJHXo0EEZGRlavXq1PD09VVBQ4PCY/Px8SZK3t7c8PT0lXVg7XPLzkmO8vLyqbW4AAAA4v0rF8HvvvacJEybokUceqep5SilZAtGuXTuH7W3bttVHH32kFi1aKCsry2Ffydf+/v4qLCy0b2vZsqXDMYGBgdU5OgAAAJxcpS7pFhYWqnPnzlU9S5mCgoLUoEEDffHFFw7b9+7dq5YtWyo8PFy7du1SUVGRfd/WrVvVunVrNW3aVIGBgfLx8dG2bdvs+0+fPq2MjAyFhYXVyGsAAACAc6pUDPfs2VMff/xxVc9SJk9PTz388MNasmSJNmzYoB9++EHLli3Tp59+qpEjR2rQoEE6c+aMpk2bpn379mn9+vV66aWXNGbMGEkX1gpHR0crPj5eW7ZsUWZmpiZMmKCAgAD169evRl4DAAAAnFOllkn0799fM2bM0M8//6yQkJAy197ee++91zqbXWxsrLy8vOz3B27Tpo0WLVqk7t27S5JWrlypOXPmKCoqSs2aNdPkyZPt64slady4cSosLNT06dNls9kUHh6upKQkPnADAADAcC7FF3+KRjldba2ti4uL9uzZU+mhnF3fvn0lSVu2bLF4ktLmrN4ub0835dkK5e3ppgae9XXyl7NX3VaZ4yVp2shuFr9iAACA0srba5W6MuyMEQgAAABUVKViuEWLFlU9BwAAAFDjKhXDixcvvuoxjz/+eGVODQAAANSYKo9hHx8f+fn5EcMAAABwepWK4czMzFLb8vLytGvXLs2cOVP/+7//e82DAQAAANWtyj5H2dvbW5GRkRo7dqz++te/VtVpAQAAgGpTZTFconnz5tq/f39VnxYAAACocpVaJlGW4uJiHTt2TCtWrOBuExZIXJsuL48q+3YCAAAYoVL1FBgYKBcXlzL3FRcXs0zCArm2QlX401MAAAAMV6kYHjt2bJkx7OPjo969e+vmm2++1rkAAACAalepGI6Li6vqOQAAAIAaV+lFpgUFBVq/fr22bdum06dPq3HjxgoLC1NUVJQ8PDyqckY4KW9PN61I+0ox9wZbPQoAAEClVCqGT58+rYceekiZmZm64YYb1KxZMx04cEAbNmzQK6+8oldffVUNGzas6lnhhPJshVaPAAAAUGmVurXa3/72Nx0/flxr1qzRBx98oJSUFH3wwQdas2aNsrOztWDBgqqeEwAAAKhylYrhLVu2aPz48QoLC3PYHhYWpnHjxum9996rkuEAAACA6lSpGM7NzdVNN91U5r6bbrpJv/zyy7XMBAAAANSISsXwLbfcog8//LDMfVu2bFGrVq2uaSgAAACgJlTqDXSjR4/WE088oYKCAg0cOFDXX3+9fvrpJ7399tt6/fXXNXPmzCoeEwAAAKh6lYrh/v376+DBg1q+fLlef/11+/b69etr7Nixuv/++6tsQAAAAKC6VCqG8/LyFBsbq+joaO3evVs5OTk6duyY7r//fl133XVVPSMAAABQLSq0ZnjPnj269957lZycLEny9fXVHXfcoTvuuEOJiYl68MEHtX///uqYEwAAAKhy5Y7hH3/8USNGjFBOTo7atm3rsM/d3V1PPfWUcnNz9eCDD+r48eNVPigAAABQ1codwy+++KIaN26sf/zjH7rrrrsc9nl5eSk6Olqpqany9vbW8uXLq3xQAAAAoKqVO4a3bt2qhx9+WI0aNbrsMU2bNtXIkSO1devWqpgNAAAAqFbljuGTJ0+W6/7B7dq1Y5kEAAAAaoVyx3CTJk2UlZV11eN+/vnnK149BgAAAJxFuWM4PDxc69evv+pxaWlp6tChwzUNBQAAANSEcsfwsGHDtG3bNs2bN0/5+fml9hcUFOi5557Tv//9bw0dOrRKhwQAAACqQ7k/dCM4OFhTp07Vs88+qzfffFM9evTQjTfeqKKiIh09elTbtm3TqVOn9Oc//1mRkZHVOTMAAABQJSr0CXRDhw5VYGCgkpKStGXLFvsV4gYNGqhnz54aNWqUQkJCqmVQAAAAoKpV+OOYb731Vt16662SpFOnTsnV1ZWPYAYAAECtVOEYvljjxo2rag4AAACgxpX7DXQAAABAXUMMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMo8okrk1X4tp0q8cAAAAoNzerB0DdkWsrtHoEAACACuHKMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjOVm9QCo3bw83JS4Nl1eHvxWAgAAtQ8Fg2uWaytUsdVDAAAAVALLJAAAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoZRpbw93bQi7SurxwAAACgXYhhVLs9WaPUIAAAA5UIMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMAwAAwFi1KoYPHDigLl26aP369fZte/bsUXR0tEJDQ9W7d28lJSU5POb8+fNauHChIiMjFRISolGjRunQoUM1PToAAACcUK2J4XPnzmnixInKy8uzbzt16pRGjhypm2++WampqYqLi9OCBQuUmppqP2bp0qVat26dZs+erZSUFLm4uCgmJkYFBQVWvAwAAAA4kVoTw4sWLVKDBg0ctr322mtyd3fXzJkz1aZNGw0aNEgjRozQihUrJEkFBQVatWqV4uLi1KtXLwUGBiohIUEnTpzQ5s2brXgZAAAAcCK1IoZ37NihlJQUPffccw7bd+7cqfDwcLm5udm3RURE6MCBA8rOzlZmZqZyc3MVERFh3+/r66ugoCDt2LGjxuYHAACAc3L6GD59+rQmT56s6dOnq3nz5g77jh8/roCAAIdtfn5+kqSjR4/q+PHjklTqcX5+fjp27Fg1Tg0AAIDawOljeObMmQoNDdXAgQNL7bPZbHJ3d3fY5uHhIUnKz8/X2bNnJanMY/Lz86tpYgAAANQWblc/xDppaWnauXOn3n777TL3e3p6lnojXEnkent7y9PTU9KFtcMlPy85xsvLq5qmBgAAQG3h1DGcmpqq7Oxs9e7d22H7jBkzlJSUpBtuuEFZWVkO+0q+9vf3V2FhoX1by5YtHY4JDAys3uEBAADg9Jw6huPj42Wz2Ry23XXXXRo3bpz69++vd955R+vWrVNRUZHq1asnSdq6datat26tpk2bqmHDhvLx8dG2bdvsMXz69GllZGQoOjq6xl8PAAAAnItTx7C/v3+Z25s2baoWLVpo0KBBWrlypaZNm6aHH35YX375pV566SXNmjVL0oW1wtHR0YqPj1eTJk3UokULzZ8/XwEBAerXr19NvhQAAAA4IaeO4atp2rSpVq5cqTlz5igqKkrNmjXT5MmTFRUVZT9m3LhxKiws1PTp02Wz2RQeHq6kpKRSb6oDAACAeWpdDH/77bcOX3fu3FkpKSmXPb5evXqaNGmSJk2aVN2jAQAAoJZx+lurAQAAANWFGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGEaV8/JwU+LadCWuTbd6FAAAgCtys3oA1E25tkKrRwAAALgqrgwDAADAWMQwAAAAjEUMo9p4e7J2GAAAODdiGNUq11bosH54RdpXFk4DAADgiBhGjcrjjXUAAMCJEMMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRw3XAyxszrB4BAACgViKG64Cz+UVWjwAAAFArEcMAAAAwFjGMGuXl4abEtelKXJtu9SgAAABys3oAmCfXVmj1CAAAAJK4MgwAAACDEcMAAAAwFjEMAAAAY7FmGNXO2/PCm+a8PNzk4uJi9TgAAAB2XBlGjci1FSovnzfOAQAA50IMAwAAwFjEMAAAAIxFDMMSJeuI+fANAABgJd5AB8vw4RsAAMBqXBkGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGIalvD3dtCLtK6vHAAAAhiKGYbk8W6HVIwAAAEMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGMRwwAAADAWMQwAAABjEcOwnJeHmxLXpitxbbrVowAAAMO4WT0AIEm5fAodAACwAFeGAQAAYCxiGAAAAMYihgEAAGAs1gzXYolr0+Xl4SYXFxerRwEAAKiVuDJci+XaCpWXzxvPAAAAKosYBgAAgLGcPoZ/+eUXPf3007rjjjvUtWtXDRkyRDt37rTv37Nnj6KjoxUaGqrevXsrKSnJ4fHnz5/XwoULFRkZqZCQEI0aNUqHDh2q6ZcBAAAAJ+T0MfzEE0/oiy++0PPPP6833nhDHTt21OjRo7V//36dOnVKI0eO1M0336zU1FTFxcVpwYIFSk1NtT9+6dKlWrdunWbPnq2UlBS5uLgoJiZGBQUFFr4qAAAAOAOnfgPdoUOH9Omnn2rt2rXq2rWrJGnatGn6+OOPtWHDBnl6esrd3V0zZ86Um5ub2rRpo0OHDmnFihUaNGiQCgoKtGrVKk2aNEm9evWSJCUkJCgyMlKbN2/WgAEDrHx5AAAAsJhTXxlu3LixXnzxRXXq1Mm+zcXFRcXFxcrJydHOnTsVHh4uN7f/Nn1ERIQOHDig7OxsZWZmKjc3VxEREfb9vr6+CgoK0o4dO2r0tQAAAMD5OHUM+/r6qlevXnJ3d7dv27Rpk3744Qf17NlTx48fV0BAgMNj/Pz8JElHjx7V8ePHJUnNmzcvdcyxY8eqeXoAAAA4O6eO4Uvt2rVLTz31lPr27as+ffrIZrM5hLIkeXh4SJLy8/N19uxZSSrzmPz8/JoZGgAAAE6r1sTw+++/r9GjR6tz5856/vnnJUmenp6l3ghXErne3t7y9PSUpDKP8fLyqoGpAQAA4MxqRQyvWbNGcXFxuuOOO7RixQp75AYEBCgrK8vh2JKv/f397csjyjrm0uUVAAAAMI/Tx/Crr76qZ555RkOHDlViYqLDkofw8HDt2rVLRUVF9m1bt25V69at1bRpUwUGBsrHx0fbtm2z7z99+rQyMjIUFhZWo68DAAAAzsepY/jAgQN69tln1a9fP40ZM0bZ2dk6efKkTp48qV9//VWDBg3SmTNnNG3aNO3bt0/r16/XSy+9pDFjxki6sFY4Ojpa8fHx2rJlizIzMzVhwgQFBASoX79+Fr86AAAAWM2p7zP87rvv6ty5c9q8ebM2b97ssC8qKkrz5s3TypUrNWfOHEVFRalZs2aaPHmyoqKi7MeNGzdOhYWFmj59umw2m8LDw5WUlFTqTXUAAAAwj1PH8KOPPqpHH330isd07txZKSkpl91fr149TZo0SZMmTarq8QAAAFDLOfUyCQAAAKA6EcMAAAAwFjEMAAAAYxHDAAAAMBYxDKeUuDZdiWvTrR4DAADUcU59NwmYK9dWaPUIAADAAFwZBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYi49jhtPw9nRT4tp0eXnw2xIAANQMqgNOJddWqGKrhwAAAMZgmQQAAACMRQzDaXl7umlF2ldWjwEAAOowYhhOLc9WaPUIAACgDiOGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGIYtUbi2nQlrk23egwAAFCHuFk9AFBeubZCq0cAAAB1DFeGAQAAYCyuDMOpeXm4KXFturw8+K0KAACqHoUBp5drK1Sx1UMAAIA6iWUSAAAAMBYxjFrF29NNK9K+snoMAABQRxDDqHXyuKsEAACoIsQwAAAAjEUMAwAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwarXEtelKXJtu9RgAAKCW4uOYUavlcs9hAABwDbgyDAAAAGMRwwAAADAWMQwAAABjEcMAAAAwFjEMAAAAYxHDAAAAMBYxDAAAAGNxn+FaKHFturw8+NYBAABcK4qqFsq1FarY6iEAAADqAJZJAAAAwFjEMAAAAIxFDAMAAMBYxDAAAACMRQwDAADAWMQwAAAAjEUMo85JXJuuxLXpVo8BAABqAe4zjDon11Zo9QgAAKCW4MowAAAAjEUMAwAAwFjEMAAAAIxFDMMovLkOAABcjDfQwSi8uQ4AAFyMK8MAAAAwFjGMOm9F2ldWjwAAAJwUMYw6L68SSyNYWwwAgBmIYRjtcleNc22FrC8GAMAAxDCMdvFVY64GAwBgHu4mUcu8vDHD6hFqHS8PNyWuTZeXx5V/u3MlGAAA8xDDtczZ/CKrR3Bql/vLQq6tUMVlbC9vKAMAgLqJAkCdUpm/LFwulAEAQN1HDKPWufRqrrfnf792cXGp1udekfaVYu4NrtbnAAAANYcYRq106dXckq8beNa/6mOvJZ4rc5s2AADgvIhh1ElXC96KxDMAAKi7uLUa6qxcW6Hy8rmSCwAALo8YBgAAgLGIYQAAABiLNcO1RE3dLQFl417EAADUTfzpXkvwhi9rlfz6u1bTX0ZKPgZ6/JCu1XJ+AABQNpZJABfx9nSr8Eder0j7qlL7LpZrK+TjoAEAsABXhoFLlPdT7C5eunK5K7vFxVz1BQDAmRHDQCVdvHTl0qu6F4cyV3wBAHBeLJMAqsGV7nFc3qUTAACg+hHDQA3jI50BAHAeLJMALuPij3Qu4eVRetu1KM+5VqR9pZh7gyu8DwAAXB0xDFxBybrgq227mrLCurznutKV5Mvt4017AACUDzEM1JDqvlfxpc8FAACujhgGqsjlrv5W5PEvb8xQdo5NUtlXdUvOfza/sNTzVOXVYK4sAwBMYcwb6M6fP6+FCxcqMjJSISEhGjVqlA4dOmT1WKhjrnQXifI4m19kv4KcuDZdL6z/sszzl/U81/LBHYlr0+0BfOm5Lt13tbthXHo8AADOzJgrw0uXLtW6des0d+5c+fv7a/78+YqJidGGDRvk7u5u9XhAKRffx/hyV5wvfUNfZT5Br+S5yrvvanfDuNK5rLjizFVuAMCVGHFluKCgQKtWrVJcXJx69eqlwMBAJSQk6MSJE9q8ebPV48EwJQF76VXfK7nSFedL95X3E/SuprJhfamLryRb8bHT5XlOrmYDgLmMiOHMzEzl5uYqIiLCvs3X11dBQUHasWOHhZPBVNe6nKKyKvqBHxX5aOrLxWTJR1KXFf8vb8yoUIhWxQeWlHWOykR6eeYmsgHA+bkUFxdX9C5Rtc57772nuLg4ffHFF/L09LRv//Of/yybzaYXXnihQucLDg5WUVGRmjdvXtWjXlbOmQK5ukguLi4qOl8sVxfpfLEuu+1K+zjejOMl6TofxyVAv+YVqKG3u/3nLnKRi4tUWHThHHKRXPTfc158/pJ9klSsYrnIxf7PkmNcXV1UWOT43GfyzpWa8eJzXXx+SWro7a5f8wrsP7/YmbxzDs9d1jFl/btz8TwX/xpc7piLf33kIvl41b/qecvz3Be73GusiKvNWB3P6Szq0msBUD2OHTumevXq6auvrnwhxYg1w2fPnpWkUmuDPTw8lJOTU+HzeXh4qKCgoEpmK68r/YELlNfF4VBTEeHjffVIu9TlZqvMuS79d6esc5f179fVfn3K8+/klY6pil//ip6jLoVjXXotAKqHm5tbud4XZkQMl1wNLigocLgynJ+fLy8vrwqfb+fOnVU2GwAAAKxjxJrhkuUMWVlZDtuzsrIUEBBgxUgAAABwAkbEcGBgoHx8fLRt2zb7ttOnTysjI0NhYWEWTgYAAAArGbFMwt3dXdHR0YqPj1eTJk3UokULzZ8/XwEBAerXr5/V4wEAAMAiRsSwJI0bN06FhYWaPn26bDabwsPDlZSUxAduAAAAGMyIW6sBAAAAZTFizTAAAABQFmIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABgLGLYSZw/f14LFy5UZGSkQkJCNGrUKB06dMjqsZza0qVLNWzYMKvHcDq//PKLnn76ad1xxx3q2rWrhgwZop07d1o9ltPJzs7WpEmTFBERoS5duuiRRx7Rvn37rB7LqR04cEBdunTR+vXrrR7F6Rw5ckTt27cv9eP111+3ejSnk5aWpv79+ys4OFgDBgzQpk2brB7JqWzbtq3M30vt27dX3759rR7PqZw7d04JCQnq3bu3unTpogcffFDp6ekVPg8x7CSWLl2qdevWafbs2UpJSZGLi4tiYmJUUFBg9WhOKTk5WQsXLrR6DKf0xBNP6IsvvtDzzz+vN954Qx07dtTo0aO1f/9+q0dzKo899ph+/PFHrVixQm+88YY8PT01YsQInT171urRnNK5c+c0ceJE5eXlWT2KU/r222/l4eGhf//73/rkk0/sPwYOHGj1aE7lzTff1FNPPaX7779fGzZsUP/+/fXEE0/o888/t3o0p9GlSxeH30OffPKJVq1aJTc3Nz366KNWj+dUli1bptTUVM2ePVtpaWm65ZZbFBMToxMnTlToPMSwEygoKNCqVasUFxenXr16KTAwUAkJCTpx4oQ2b95s9XhO5cSJE3r44Ye1YMECtW7d2upxnM6hQ4f06aefasaMGQoLC9Mtt9yiadOmyd/fXxs2bLB6PKdx6tQp3XjjjXrmmWcUHBysNm3aKDY2VidPntR3331n9XhOadGiRWrQoIHVYzitvXv3qnXr1vLz81OzZs3sPzw9Pa0ezWkUFxdrwYIFGj58uIYPH65WrVpp7Nixuu2227R9+3arx3Ma7u7uDr+HGjVqpLlz5+quu+7S4MGDrR7PqWzZskV33323evbsqVatWunJJ5/UmTNntHv37gqdhxh2ApmZmcrNzVVERIR9m6+vr4KCgrRjxw4LJ3M+33zzja677jq99dZbCgkJsXocp9O4cWO9+OKL6tSpk32bi4uLiouLlZOTY+FkzqVx48Z6/vnn9Zvf/EaS9NNPPykpKUkBAQFq27atxdM5nx07diglJUXPPfec1aM4rW+//ZbfO1fx/fff68iRI6WuliclJWnMmDEWTeX8XnnlFR07dkxTp061ehSn06hRI3344Yc6fPiwioqKlJKSInd3d3Xo0KFC53GrpvlQAcePH5ckNW/e3GG7n5+fjh07ZsVITqtPnz7q06eP1WM4LV9fX/Xq1cth26ZNm/TDDz+oZ8+eFk3l3P73f/9Xr732mtzd3bVs2TJ5e3tbPZJTOX36tCZPnqzp06eX+m8U/mvv3r1q1qyZHnzwQR08eFCtWrVSbGysIiMjrR7NaRw8eFCSlJeXp9GjRysjI0M33nijHnvsMf67fhn5+flavny5hg8fLj8/P6vHcTrTpk3ThAkT1LdvX9WrV0+urq5asGCBWrZsWaHzcGXYCZSsUXR3d3fY7uHhofz8fCtGQh2xa9cuPfXUU+rbty9/2FzG8OHDlZqaqnvuuUdjx47VN998Y/VITmXmzJkKDQ1l7esVFBQU6ODBgzpz5ozGjx+vF198UcHBwYqJidHWrVutHs9pnDlzRpI0ZcoU3X333Vq1apVuv/12xcbG8ut0GW+++aby8/N5s/hl7N+/X76+vlqyZIlSUlJ03333acqUKcrMzKzQebgy7ARK1pQVFBQ4rC/Lz8+Xl5eXVWOhlnv//fc1ceJEhYSE6Pnnn7d6HKdV8r+2n3nmGe3evVtr1qzR3LlzLZ7KOaSlpWnnzp16++23rR7Fqbm7u2vHjh1yc3OzX9To1KmT9u/fr6SkJPXo0cPiCZ1D/fr1JUmjR49WVFSUJKlDhw7KyMjQ6tWr+XUqQ1pamu666y41btzY6lGczpEjRzRp0iQlJycrLCxMkhQcHKx9+/Zp0aJFWrJkSbnPxZVhJ1Dyvx6zsrIctmdlZSkgIMCKkVDLrVmzRnFxcbrjjju0YsUK3sRziezsbG3YsEFFRUX2ba6urmrTpk2pfw9NlpqaquzsbPtti7p06SJJmjFjhgYMGGDxdM7F29u71P/da9euXYXf1V6Xlfx51q5dO4ftbdu21eHDh60Yyan9/PPP+vzzz9W/f3+rR3FKX375pc6dO6fg4GCH7SEhIfYlOeVFDDuBwMBA+fj4aNu2bfZtp0+fVkZGhv1vO0B5vfrqq3rmmWc0dOhQJSYmlvoDGhf+ovmXv/zF4R3s586dU0ZGhtq0aWPhZM4lPj5eGzduVFpamv2HJI0bN04vvviitcM5kczMTHXp0qXU/by//vpr3lR3kaCgIDVo0EBffPGFw/a9e/dWeI2nCdLT0+Xi4qJu3bpZPYpTKrmQ+O233zps37t3r1q1alWhc7FMwgm4u7srOjpa8fHxatKkiVq0aKH58+crICBA/fr1s3o81CIHDhzQs88+q379+mnMmDHKzs627/P09FTDhg0tnM55BAYGqmfPnpo1a5Zmz54tX19fLV++XKdPn9aIESOsHs9p+Pv7l7m9adOmatGiRQ1P47zatWun3/zmN5o1a5ZmzJihxo0b67XXXtPu3bv1xhtvWD2e0/D09NTDDz+sJUuWyN/fX507d9Y777yjTz/9VMnJyVaP53QyMzN10003sVzyMjp37qywsDBNmTJFM2bMUEBAgNLS0rR161a9+uqrFToXMewkxo0bp8LCQk2fPl02m03h4eFKSkriqh4q5N1339W5c+e0efPmUveojoqK0rx58yyazLm4uLgoMTFRf/vb3zR+/Hj9+uuvCgsL0yuvvKIbbrjB6vFQy7i6umr58uWKj4/X+PHjdfr0aQUFBWn16tVq37691eM5ldjYWHl5ednvpd+mTRstWrRI3bt3t3o0p/PTTz+pUaNGVo/htFxdXbV06VIlJiZq6tSpysnJUbt27ZScnKzQ0NAKnculuLi4uHrGBAAAAJwba4YBAABgLGIYAAAAxiKGAQAAYCxiGAAAAMYihgEAAGAsYhgAAADGIoYBAABguaVLl2rYsGEVflxaWpr69++v4OBgDRgwQJs2barQ44lhAKjlhg0bpvbt2zv86NSpk3r37q1Zs2YpJyfnso89fPiw2rdvr/Xr19fgxADgKDk5WQsXLqzw495880099dRTuv/++7Vhwwb1799fTzzxhD7//PNyn4NPoAOAOiAoKEgzZsywf33u3Dl98803ev7557Vnzx6tXbtWLi4upR7n5+enlJQUtWzZsibHBQBJ0okTJzRt2jTt2rVLrVu3rtBji4uLtWDBAg0fPlzDhw+XJI0dO1bp6enavn27unTpUq7zEMMAUAf4+PiU+gjS8PBw5ebmauHChfriiy/K/IhSd3f3Cn90KQBUlW+++UbXXXed3nrrLS1ZskRHjhxx2P/hhx9q0aJF2rdvn/z9/TVgwADFxsbK3d1d33//vY4cOaKBAwc6PCYpKalCM7BMAgDqsE6dOkmSjh49qmHDhmnixIkaN26cunbtqkceeaTMZRI//PCDxo0bp27duik8PFwxMTH67rvv7Pvz8/P117/+Vb169VKnTp00cOBAbdy4scZfG4Dar0+fPvrb3/6mm266qdS+jz/+WH/+8581ePBgbdiwQTNmzNCmTZs0adIkSdLBgwclSXl5eRo9erR69OihwYMH64MPPqjQDMQwANRhBw4ckCT7HzSbNm1S/fr1tWTJEj300EOljs/KytLgwYP1/fffa8aMGYqPj1dOTo5GjBihn3/+WcXFxRo7dqzWrVunkSNHatmyZerSpYsmTJigtLS0mnxpAOq45cuX649//KOGDBmili1bqmfPnpo1a5b++c9/6vDhwzpz5owkacqUKbr77ru1atUq3X777YqNjdXWrVvL/TwskwCAOqC4uFiFhYX2r3NycrR9+3YtW7ZMoaGh9ivErq6ueuaZZ+Tt7S3pwhvoLrZ69WrZbDatXr1azZo1kyR16NBB999/v3bv3i0PDw/9+9//VkJCgvr37y9JioyM1NmzZxUfH6+7775bbm780QLg2mVkZOjLL7/UP/7xD/u24uJiSdL+/ftVv359SdLo0aMVFRUl6cJ/rzIyMrR69Wr16NGjXM/Df7EAoA7YsWOHOnbs6LDN1dVVPXr00DPPPGN/89yNN95oD+Gy7Nq1S6GhofYQli68ye7DDz+UJMXHx8vFxUW9evVyiO8+ffrorbfe0nfffacOHTpU5UsDYKjz58/r4YcftofuxZo1a6bMzExJUrt27Rz2tW3bVh999FG5n4cYBoA6oGPHjpo1a5YkycXFRR4eHmrevLl8fHwcjrv++uuveJ5ffvlFN9544xX3FxcXq2vXrmXuz8rKIoYBVInf/OY3+v7779WqVSv7tu3bt+ull17SzJkzFRQUpAYNGuiLL75QWFiY/Zi9e/dW6A45xDAA1AENGjRQcHDwNZ+nYcOG+vnnn0tt37p1q2688UY1bNhQ3t7e+vvf/17m4y/+QwsArkVMTIzGjx+vRYsW6e6779bx48c1ffp03XDDDfb/e/Xwww9ryZIl8vf3V+fOnfXOO+/o008/VXJycrmfhzfQAQDswsLCtHv3bmVnZ9u3/fzzz4qJidGWLVvUrVs35eXlqbi4WMHBwfYf3333nZYsWeKwdAIArsXvf/97JSQkaMuWLRo4cKAmTpyoHj16aPHixfZjYmNjFRcXZ38fwz//+U8tWrRI3bt3L/fzcGUYAGA3YsQIpaWlafTo0Xr00Ufl4eGhF154QX5+frr33nvl6+ur8PBwxcbGKjY2Vm3atNGXX36pRYsWqWfPnmrSpInVLwFALTVv3rxS2/7whz/oD3/4wxUfN3LkSI0cObLSz0sMAwDsmjdvrldffVXz58/X1KlT5e7urm7dumn+/Plq1KiRJOnFF1/UggUL9MILLyg7O1v+/v4aMWKExo4da+3wAFAJLsUl96gAAAAADMOaYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLGIYQAAABiLGAYAAICxiGEAAAAYixgGAACAsYhhAAAAGIsYBgAAgLH+H3XXb5RfV+RoAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Since price is our target, we will explore it first\n",
+    "#view distribution of price using histogram\n",
+    "sns.set(style = 'white')\n",
+    "fig, ax = plt.subplots(figsize = (8,8))\n",
+    "sns.histplot(data = df, x = 'price', palette = 'Dark', )\n",
+    "ax.set_xlabel('Price')\n",
+    "ax.set_ylabel('Count')\n",
+    "ax.set_title('Distribution of Price')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We notice from the histogram that the price data is skewed to the right, indicating a non-normal distribution.\n",
+    "\n",
+    "To normalize the price data, we log-transformed (base of e) the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Since the data is skewed to the right, transform the price data using log\n",
+    "df['log_price'] = np.log(df['price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#view distribution of log base e for price using histogram\n",
+    "sns.set(style = 'white')\n",
+    "fig, ax = plt.subplots(figsize = (6,6))\n",
+    "sns.histplot(data = df, x = 'log_price', palette = 'Dark')\n",
+    "ax.set_xlabel('Natural Log of Price')\n",
+    "ax.set_ylabel('Count')\n",
+    "ax.set_title('Distribution of Natural Log of Price')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Upon applying the log transformation, we observed that the distribution of house prices became more symmetrical and closer to a normal distribution as shown in the above histogram."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Visualizing and Transforming Numeric Features**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before the predictive model, we have to understand the distribution of numeric features in the dataset to identify any skewness or outliers. Here, we plotted histograms for each feature to provide the spread and shape of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting the numeric features to see distribution and see if they need to be log-transformed\n",
+    "df[numeric].hist(figsize=[6,6]);\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As shown above, some numeric features exhibited skewness or non-normal distributions. Thus, we apply log transformations to these numeric features selectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Log transform all numeric feature except:\n",
+    "# sqft basement - has values of 0\n",
+    "# long - has negative values\n",
+    "to_ln = ['bedrooms',\n",
+    "         'bathrooms',\n",
+    "         'sqft_living',\n",
+    "         'sqft_lot', \n",
+    "         'sqft_above',\n",
+    "         'lat',\n",
+    "         'sqft_living15', \n",
+    "         'sqft_lot15']\n",
+    "\n",
+    "for column in to_ln:\n",
+    "    df[column] = np.log(df[column])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Resulting dataframes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#create two dataframes, one without log_price, and without price\n",
+    "output = df.drop(['log_price'], axis=1) \n",
+    "output_log = df.drop(['price'], axis=1) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "price            float64\n",
+       "bedrooms         float64\n",
+       "bathrooms        float64\n",
+       "sqft_living      float64\n",
+       "sqft_lot         float64\n",
+       "floors           float64\n",
+       "waterfront         int64\n",
+       "view               int64\n",
+       "condition          int64\n",
+       "grade              int32\n",
+       "sqft_above       float64\n",
+       "sqft_basement    float64\n",
+       "yr_built           int64\n",
+       "yr_renovated     float64\n",
+       "zipcode            int64\n",
+       "lat              float64\n",
+       "long             float64\n",
+       "sqft_living15    float64\n",
+       "sqft_lot15       float64\n",
+       "month_of_date      int32\n",
+       "year_of_date       int32\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***2. Multiple Linear Regression Analysis***\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Multiple linear regression was employed to understand how features collectively influence the target variable ( housing prices). We partitioned the dataset into training and testing sets to train the regression model on one subset and assess its performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+    "import pandas as pd\n",
+    "\n",
+    "def train_test(df, target, test_size=0.20, random_state=42):\n",
+    "    '''\n",
+    "    This function takes in a dataframe df and target column and returns the train and test split\n",
+    "    Default test size is 20, default random state is 42\n",
+    "    '''\n",
+    "    \n",
+    "    # Drop rows with missing values\n",
+    "    df = df.dropna()\n",
+    "    \n",
+    "    # Separating predictors (X) and target (y)\n",
+    "    X = df.drop(target, axis=1)\n",
+    "    y = df[target]\n",
+    "    \n",
+    "    # Creating train-test split\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)\n",
+    "    \n",
+    "    # Resetting indices to ensure alignment\n",
+    "    X_train.reset_index(drop=True, inplace=True)\n",
+    "    X_test.reset_index(drop=True, inplace=True)\n",
+    "    y_train.reset_index(drop=True, inplace=True)\n",
+    "    y_test.reset_index(drop=True, inplace=True)\n",
+    "    \n",
+    "    # Selecting categorical columns\n",
+    "    categorical = X.select_dtypes(include=['object']).columns.tolist()\n",
+    "    # Instantiating OneHotEncoder object\n",
+    "    ohe = OneHotEncoder(sparse_output=False, handle_unknown='error', drop='first')\n",
+    "    \n",
+    "    # Fitting and transforming categorical features on train and test sets\n",
+    "    X_train_ohe = ohe.fit_transform(X_train[categorical])\n",
+    "    X_test_ohe = ohe.transform(X_test[categorical])\n",
+    "\n",
+    "    # Placing column names onto new categorical columns and formatting as DataFrame\n",
+    "#     X_train_ohe_df = pd.DataFrame(X_train_ohe, columns=ohe.get_feature_names(categorical))\n",
+    "#     X_test_ohe_df = pd.DataFrame(X_test_ohe, columns=ohe.get_feature_names(categorical))\n",
+    "\n",
+    "    # Get feature names for one-hot encoded columns\n",
+    "    feature_names = []\n",
+    "    for cat, categories in zip(categorical, ohe.categories_):\n",
+    "        feature_names.extend([f\"{cat}_{val}\" for val in categories[1:]])  # Skip the first category\n",
+    "        \n",
+    "    # Placing column names onto new categorical columns and formatting as DataFrame\n",
+    "    X_train_ohe_df = pd.DataFrame(X_train_ohe, columns=feature_names)\n",
+    "    X_test_ohe_df = pd.DataFrame(X_test_ohe, columns=feature_names)\n",
+    "    \n",
+    "    # Combining categoricals with rest of data\n",
+    "    X_train = pd.concat([X_train.select_dtypes(include=['number']), X_train_ohe_df], axis=1)\n",
+    "    X_test = pd.concat([X_test.select_dtypes(include=['number']), X_test_ohe_df], axis=1)\n",
+    "\n",
+    "    # List to hold X_train and X_test\n",
+    "    X_list = [X_train, X_test]\n",
+    "    \n",
+    "    # Scaling X values into z-scores\n",
+    "    ss = StandardScaler()\n",
+    "    for i in range(len(X_list)):\n",
+    "        X_list[i] = pd.DataFrame(ss.fit_transform(X_list[i]), columns=X_list[i].columns)\n",
+    "        \n",
+    "    # Unpacking the list\n",
+    "    X_train, X_test = X_list\n",
+    "    \n",
+    "    return X_train, X_test, y_train, y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: scikit-learn in c:\\users\\hp\\anaconda3\\lib\\site-packages (1.4.2)\n",
+      "Requirement already satisfied: numpy>=1.19.5 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from scikit-learn) (1.24.3)\n",
+      "Requirement already satisfied: scipy>=1.6.0 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from scikit-learn) (1.11.1)\n",
+      "Requirement already satisfied: joblib>=1.2.0 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from scikit-learn) (1.2.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from scikit-learn) (2.2.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip3 install -U scikit-learn\n",
+    "# Use a multiple linear regression model of the data using all features\n",
+    "# Split data into train and test\n",
+    "X_train, X_test, y_train, y_test = train_test(output, 'price')\n",
+    "\n",
+    "#create linear regression model for price. \n",
+    "model1 = LinearRegression()\n",
+    "model1.fit(X_train, y_train);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot actual vs predicted values\n",
+    "def actual_vs_predicted(model,X_test,y_test):\n",
+    "    \"\"\"\n",
+    "    Plots the actual y vs the predicted y\n",
+    "    \"\"\"\n",
+    "    y_predicted = model.predict(X_test)\n",
+    "    fig, ax = plt.subplots(figsize=(10,10))\n",
+    "    ax.scatter(x=y_test, y=y_predicted)\n",
+    "    ax.set_xlabel(\"Actual Price Values\")\n",
+    "    ax.set_ylabel(\"Predicted Price Values\")\n",
+    "    ax.set_title(\"Actual vs Predicted\")\n",
+    "    \n",
+    "    p1 = max(max(y_test), max(y_predicted))\n",
+    "    p2 = min(min(y_test), min(y_predicted))\n",
+    "    plt.plot([p1, p2], [p1, p2], 'b-')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot actual vs predicted\n",
+    "actual_vs_predicted(model1, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#add constant to X_train\n",
+    "X_train = sm.add_constant(X_train)\n",
+    "\n",
+    "#find OLS for train data set\n",
+    "model1_ols = sm.OLS(y_train, X_train).fit()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th>  <td>   0.676</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.676</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   1761.</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Mon, 29 Apr 2024</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>10:29:40</td>     <th>  Log-Likelihood:    </th> <td>-2.3130e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td> 16914</td>      <th>  AIC:               </th>  <td>4.626e+05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td> 16893</td>      <th>  BIC:               </th>  <td>4.628e+05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>    20</td>      <th>                     </th>      <td> </td>     \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>         <td> 5.407e+05</td> <td> 1617.576</td> <td>  334.271</td> <td> 0.000</td> <td> 5.38e+05</td> <td> 5.44e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>bedrooms</th>      <td>-2.091e+04</td> <td> 2226.108</td> <td>   -9.393</td> <td> 0.000</td> <td>-2.53e+04</td> <td>-1.65e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>bathrooms</th>     <td> 1.958e+04</td> <td> 2994.316</td> <td>    6.539</td> <td> 0.000</td> <td> 1.37e+04</td> <td> 2.55e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_living</th>   <td>-2.483e+05</td> <td> 9509.150</td> <td>  -26.112</td> <td> 0.000</td> <td>-2.67e+05</td> <td> -2.3e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_lot</th>      <td> 4223.5163</td> <td> 4260.950</td> <td>    0.991</td> <td> 0.322</td> <td>-4128.390</td> <td> 1.26e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>floors</th>        <td> 6429.9215</td> <td> 2527.189</td> <td>    2.544</td> <td> 0.011</td> <td> 1476.367</td> <td> 1.14e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>waterfront</th>    <td>  6.18e+04</td> <td> 1661.167</td> <td>   37.205</td> <td> 0.000</td> <td> 5.85e+04</td> <td> 6.51e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>view</th>          <td> 2.133e+04</td> <td> 1753.237</td> <td>   12.168</td> <td> 0.000</td> <td> 1.79e+04</td> <td> 2.48e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>condition</th>     <td>-1.863e+04</td> <td> 1795.649</td> <td>  -10.376</td> <td> 0.000</td> <td>-2.22e+04</td> <td>-1.51e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>grade</th>         <td> 1.593e+05</td> <td> 2903.227</td> <td>   54.880</td> <td> 0.000</td> <td> 1.54e+05</td> <td> 1.65e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_above</th>    <td> 3.167e+05</td> <td> 8551.766</td> <td>   37.030</td> <td> 0.000</td> <td>    3e+05</td> <td> 3.33e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_basement</th> <td> 1.779e+05</td> <td> 4581.046</td> <td>   38.825</td> <td> 0.000</td> <td> 1.69e+05</td> <td> 1.87e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>yr_built</th>      <td>-7.931e+04</td> <td> 2519.279</td> <td>  -31.482</td> <td> 0.000</td> <td>-8.42e+04</td> <td>-7.44e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>yr_renovated</th>  <td>  1.08e+04</td> <td> 1709.602</td> <td>    6.315</td> <td> 0.000</td> <td> 7444.537</td> <td> 1.41e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>zipcode</th>       <td>-2.474e+04</td> <td> 2092.666</td> <td>  -11.821</td> <td> 0.000</td> <td>-2.88e+04</td> <td>-2.06e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>lat</th>           <td>  7.98e+04</td> <td> 1765.242</td> <td>   45.206</td> <td> 0.000</td> <td> 7.63e+04</td> <td> 8.33e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>long</th>          <td>-2.077e+04</td> <td> 2213.351</td> <td>   -9.385</td> <td> 0.000</td> <td>-2.51e+04</td> <td>-1.64e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_living15</th> <td> 2.883e+04</td> <td> 2731.845</td> <td>   10.555</td> <td> 0.000</td> <td> 2.35e+04</td> <td> 3.42e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_lot15</th>    <td>-1.523e+04</td> <td> 4171.793</td> <td>   -3.650</td> <td> 0.000</td> <td>-2.34e+04</td> <td>-7051.217</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>month_of_date</th> <td> 4661.3018</td> <td> 2596.610</td> <td>    1.795</td> <td> 0.073</td> <td> -428.325</td> <td> 9750.928</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>year_of_date</th>  <td> 1.854e+04</td> <td> 2601.202</td> <td>    7.127</td> <td> 0.000</td> <td> 1.34e+04</td> <td> 2.36e+04</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>14575.210</td> <th>  Durbin-Watson:     </th>  <td>   2.001</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>1418263.298</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>           <td> 3.658</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>       <td>47.260</td>   <th>  Cond. No.          </th>  <td>    18.9</td>  \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &      0.676   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &      0.676   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &      1761.   \\\\\n",
+       "\\textbf{Date:}             & Mon, 29 Apr 2024 & \\textbf{  Prob (F-statistic):} &      0.00    \\\\\n",
+       "\\textbf{Time:}             &     10:29:40     & \\textbf{  Log-Likelihood:    } & -2.3130e+05  \\\\\n",
+       "\\textbf{No. Observations:} &       16914      & \\textbf{  AIC:               } &  4.626e+05   \\\\\n",
+       "\\textbf{Df Residuals:}     &       16893      & \\textbf{  BIC:               } &  4.628e+05   \\\\\n",
+       "\\textbf{Df Model:}         &          20      & \\textbf{                     } &              \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &              \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                         & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}           &    5.407e+05  &     1617.576     &   334.271  &         0.000        &     5.38e+05    &     5.44e+05     \\\\\n",
+       "\\textbf{bedrooms}        &   -2.091e+04  &     2226.108     &    -9.393  &         0.000        &    -2.53e+04    &    -1.65e+04     \\\\\n",
+       "\\textbf{bathrooms}       &    1.958e+04  &     2994.316     &     6.539  &         0.000        &     1.37e+04    &     2.55e+04     \\\\\n",
+       "\\textbf{sqft\\_living}    &   -2.483e+05  &     9509.150     &   -26.112  &         0.000        &    -2.67e+05    &     -2.3e+05     \\\\\n",
+       "\\textbf{sqft\\_lot}       &    4223.5163  &     4260.950     &     0.991  &         0.322        &    -4128.390    &     1.26e+04     \\\\\n",
+       "\\textbf{floors}          &    6429.9215  &     2527.189     &     2.544  &         0.011        &     1476.367    &     1.14e+04     \\\\\n",
+       "\\textbf{waterfront}      &     6.18e+04  &     1661.167     &    37.205  &         0.000        &     5.85e+04    &     6.51e+04     \\\\\n",
+       "\\textbf{view}            &    2.133e+04  &     1753.237     &    12.168  &         0.000        &     1.79e+04    &     2.48e+04     \\\\\n",
+       "\\textbf{condition}       &   -1.863e+04  &     1795.649     &   -10.376  &         0.000        &    -2.22e+04    &    -1.51e+04     \\\\\n",
+       "\\textbf{grade}           &    1.593e+05  &     2903.227     &    54.880  &         0.000        &     1.54e+05    &     1.65e+05     \\\\\n",
+       "\\textbf{sqft\\_above}     &    3.167e+05  &     8551.766     &    37.030  &         0.000        &        3e+05    &     3.33e+05     \\\\\n",
+       "\\textbf{sqft\\_basement}  &    1.779e+05  &     4581.046     &    38.825  &         0.000        &     1.69e+05    &     1.87e+05     \\\\\n",
+       "\\textbf{yr\\_built}       &   -7.931e+04  &     2519.279     &   -31.482  &         0.000        &    -8.42e+04    &    -7.44e+04     \\\\\n",
+       "\\textbf{yr\\_renovated}   &     1.08e+04  &     1709.602     &     6.315  &         0.000        &     7444.537    &     1.41e+04     \\\\\n",
+       "\\textbf{zipcode}         &   -2.474e+04  &     2092.666     &   -11.821  &         0.000        &    -2.88e+04    &    -2.06e+04     \\\\\n",
+       "\\textbf{lat}             &     7.98e+04  &     1765.242     &    45.206  &         0.000        &     7.63e+04    &     8.33e+04     \\\\\n",
+       "\\textbf{long}            &   -2.077e+04  &     2213.351     &    -9.385  &         0.000        &    -2.51e+04    &    -1.64e+04     \\\\\n",
+       "\\textbf{sqft\\_living15}  &    2.883e+04  &     2731.845     &    10.555  &         0.000        &     2.35e+04    &     3.42e+04     \\\\\n",
+       "\\textbf{sqft\\_lot15}     &   -1.523e+04  &     4171.793     &    -3.650  &         0.000        &    -2.34e+04    &    -7051.217     \\\\\n",
+       "\\textbf{month\\_of\\_date} &    4661.3018  &     2596.610     &     1.795  &         0.073        &     -428.325    &     9750.928     \\\\\n",
+       "\\textbf{year\\_of\\_date}  &    1.854e+04  &     2601.202     &     7.127  &         0.000        &     1.34e+04    &     2.36e+04     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 14575.210 & \\textbf{  Durbin-Watson:     } &      2.001   \\\\\n",
+       "\\textbf{Prob(Omnibus):} &    0.000  & \\textbf{  Jarque-Bera (JB):  } & 1418263.298  \\\\\n",
+       "\\textbf{Skew:}          &    3.658  & \\textbf{  Prob(JB):          } &       0.00   \\\\\n",
+       "\\textbf{Kurtosis:}      &   47.260  & \\textbf{  Cond. No.          } &       18.9   \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.676\n",
+       "Model:                            OLS   Adj. R-squared:                  0.676\n",
+       "Method:                 Least Squares   F-statistic:                     1761.\n",
+       "Date:                Mon, 29 Apr 2024   Prob (F-statistic):               0.00\n",
+       "Time:                        10:29:40   Log-Likelihood:            -2.3130e+05\n",
+       "No. Observations:               16914   AIC:                         4.626e+05\n",
+       "Df Residuals:                   16893   BIC:                         4.628e+05\n",
+       "Df Model:                          20                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "=================================================================================\n",
+       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "---------------------------------------------------------------------------------\n",
+       "const          5.407e+05   1617.576    334.271      0.000    5.38e+05    5.44e+05\n",
+       "bedrooms      -2.091e+04   2226.108     -9.393      0.000   -2.53e+04   -1.65e+04\n",
+       "bathrooms      1.958e+04   2994.316      6.539      0.000    1.37e+04    2.55e+04\n",
+       "sqft_living   -2.483e+05   9509.150    -26.112      0.000   -2.67e+05    -2.3e+05\n",
+       "sqft_lot       4223.5163   4260.950      0.991      0.322   -4128.390    1.26e+04\n",
+       "floors         6429.9215   2527.189      2.544      0.011    1476.367    1.14e+04\n",
+       "waterfront      6.18e+04   1661.167     37.205      0.000    5.85e+04    6.51e+04\n",
+       "view           2.133e+04   1753.237     12.168      0.000    1.79e+04    2.48e+04\n",
+       "condition     -1.863e+04   1795.649    -10.376      0.000   -2.22e+04   -1.51e+04\n",
+       "grade          1.593e+05   2903.227     54.880      0.000    1.54e+05    1.65e+05\n",
+       "sqft_above     3.167e+05   8551.766     37.030      0.000       3e+05    3.33e+05\n",
+       "sqft_basement  1.779e+05   4581.046     38.825      0.000    1.69e+05    1.87e+05\n",
+       "yr_built      -7.931e+04   2519.279    -31.482      0.000   -8.42e+04   -7.44e+04\n",
+       "yr_renovated    1.08e+04   1709.602      6.315      0.000    7444.537    1.41e+04\n",
+       "zipcode       -2.474e+04   2092.666    -11.821      0.000   -2.88e+04   -2.06e+04\n",
+       "lat             7.98e+04   1765.242     45.206      0.000    7.63e+04    8.33e+04\n",
+       "long          -2.077e+04   2213.351     -9.385      0.000   -2.51e+04   -1.64e+04\n",
+       "sqft_living15  2.883e+04   2731.845     10.555      0.000    2.35e+04    3.42e+04\n",
+       "sqft_lot15    -1.523e+04   4171.793     -3.650      0.000   -2.34e+04   -7051.217\n",
+       "month_of_date  4661.3018   2596.610      1.795      0.073    -428.325    9750.928\n",
+       "year_of_date   1.854e+04   2601.202      7.127      0.000    1.34e+04    2.36e+04\n",
+       "==============================================================================\n",
+       "Omnibus:                    14575.210   Durbin-Watson:                   2.001\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1418263.298\n",
+       "Skew:                           3.658   Prob(JB):                         0.00\n",
+       "Kurtosis:                      47.260   Cond. No.                         18.9\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model1_ols.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The multiple linear regression model effectively explains a significant portion of the variance in housing prices. R-squared is 0.676, indicating that approximately 67.6% of the variability in housing prices is accounted for by the independent variables. The F-statistic is 1761 with a p-value of 0.00, indicating that the regression model is highly significant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Normality Test**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We assess the normality of residuals. By examining the results we are able to determine whether the assumption of normality holds for our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to perform a normality test\n",
+    "import statsmodels.stats.api as sms\n",
+    "\n",
+    "def normality_test(ols_model):\n",
+    "    \"\"\"\n",
+    "    tests for normality by taking in an OLS model, and reporting out different test features\n",
+    "    and plots the Q-Q plot\n",
+    "    \"\"\"\n",
+    "    residuals = ols_model.resid\n",
+    "    name = ['Jarque-Bera','Prob','Skew', 'Kurtosis']\n",
+    "    test = sms.jarque_bera(residuals)\n",
+    "    for name, test in zip(name, test):\n",
+    "        print('\\n',name, '----')\n",
+    "        print(test)\n",
+    "    fig = sm.graphics.qqplot(residuals, dist=stats.norm, line='45', fit=True);\n",
+    "    fig.show();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Jarque-Bera ----\n",
+      "1418263.297907576\n",
+      "\n",
+      " Prob ----\n",
+      "0.0\n",
+      "\n",
+      " Skew ----\n",
+      "3.6580060662260476\n",
+      "\n",
+      " Kurtosis ----\n",
+      "47.25958185201527\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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b2eTsWCKVkjUvj6OL3yZl4w8ABLduRaPRz+IZHOzcYHJDVG5EXNCWPUksWbeX1Iz84m1hQd481bsFnVrWcGIykcon5/hx4l6fRd7pJDCbqfPYI9Tq+xAms9nZ0eQGqdyIuJgte5KYvmL7ZdtTM/KZvmI7Lwxor4IjUgoMw+Dcf77h2NJ3sVkseIaFEjN2NEHNmjo7mtwklRsRF2K1GSxZt/eaY97+bB8dmlfXFJXITSjKzeXowsWc/3EzACG3tKHRqGfxCAx0cjIpDSo3Ii7kQEJqiamoKzmfnseBhFRaNKxWTqlEKpfshATiXp9F/pmzYDZTt//j1Oz9gKahKhGVGxEXkpZ57WLj6DgR+X+GYXD2y39z7J3lGEVFeFarRuz4MQQ2jnV2NCllKjciLiQ00LtUx4nIRUXZORxZsJDUrf8FIPTW9jR89mk8AgKcnEzKgsqNiAtpGh1GWJD3NaemqgX70DRaD+sTsVfW4SPEzZhFwblkTO7u1P1rP2o8cB8mk65bq6w0wSjiQtzMJp7qfe0Fw4Y82FwXE4vYwTAMktZ/wd7nJ1FwLhmviAhaTP8HNR+8X8WmktOZGxEX06llDV4Y0P6ydW6qBfsw5MHmug1cxA6FWVkcmf8madsuLqsQ1rEDDUc+jbu/n5OTSXlQuRFxQZ1a1qBD8+paoVjkBmQeiiN+5mwKUs5jcnen/uABRP3xXp2tqUJUbkRclJvZpNu9RRxg2GycXreexPdWYViteEdFETthLP4Nop0dTcqZyo2IiFR4hZmZHJ77Bhd2/gJAtTtup8HTw3D39XVyMnEGlRsREanQMvYfIH7WHCypaZg8PIgeMpjIu+/SNFQVpnIjIiIVkmGzceqTT0lc9QHYbHjXqEHjiWPxq1fP2dHEyVRuRESkwrGkZ3B4zjzSd+0GILxbFxoMewo3Hx8nJxNXoHIjIiIVSvqevcTPnkvhhXTMnp5ED32SiJ49NA0lxVRuRESkQjCsVk5+9AknP/wIbDZ8atei8YSx+Nap4+xo4mJUbkRExOVZ0i4QP2ceGXv2AhDRswfRTz2Bm7eesyaXU7kRERGXlr5rN/Gz51GYkYHZ25sGw4YQ0b2bs2OJC3O5crNw4UK2bt3KypUri7e98MILrF27tsS4yMhINm3aVN7xRESknBhWK4mrP+TUx2vBMPCtW4fYCWPxrVXL2dHExblUuVm+fDnz58+nffv2JbbHxcUxbNgw+vXrV7zNzc2tvOOJlAurzdBjF6TKK0hNJX7mHDIPHAQg8p67qP/EINy8vJycTCoClyg3586dY9KkSezcuZP69euXeM1qtXLkyBFGjBhBeHi4kxKKlI8te5Iue2BmWJA3T/VuoQdmSpVxYecvxM99g6LMTMze3jR8ejjhXe5wdiypQMzODgCwf/9+goKCWL9+Pa1atSrx2vHjxykoKKBBgwZOSidSPrbsSWL6iu0lig1AakY+01dsZ8ueJCclEykftqIijq9YyYH/mUpRZiZ+0fVpPWeGio04zCXO3PTo0YMePXpc8bX4+HhMJhMrVqxg06ZNmM1munbtyqhRowgICCjnpCJlw2ozWLJu7zXHvP3ZPjo0r64pKqmUClJSiJs5h6xDcQBE/fEP1B80ALOnp5OTSUXkEuXmWg4fPozZbKZmzZosXryYEydO8NprrxEfH8+KFSswm13i5JPITTmQkHrZGZvfO5+ex4GEVD0pXCqd1J+3c2T+Aoqys3Hz9aXhyBFUu72js2NJBeby5eaZZ55h4MCBBAYGAhATE0N4eDgPP/wwe/fuvWwaS6QiSsu8drFxdJxIRWArLOTEP98jaf0XAPg3bEDs+DF4R0U5OZlUdC5fbkwmU3GxuSQmJgaAs2fPqtxIpRAaaN9CZPaOE3F1+efOETdjDtmHDwNQ/f77qDegH2YPDycnk8rA5cvN2LFjSU9PZ9myZcXb9u69eG1Cw4YNnRVLpFQ1jQ4jLMj7mlNT1YJ9aBodVo6pRMpG6tb/cviNN7Hm5OLm50ej50YS1uFWZ8eSSsTlL1i577772Lx5M4sWLSIxMZEffviBF198kfvuu093UEml4WY28VTvFtccM+TB5rqYWCo0W2EhCUuWcujVGVhzcgmIjaH13JkqNlLqXP7MTffu3Zk3bx6LFy9m8eLFBAQEcP/99zNq1ChnRxMpVZ1a1uCFAe0vW+emWrAPQx5srnVupELLO3OGuBmzyTmaAEDNhx6kTr/HMLu7/I8hqYBMhmEYzg5Rnnr27AnAhg0bnJxE5Mq0QrFUNud/2syRBYuw5uXhHhBAo1HPENruFmfHkgrGkZ/fqswiLsbNbNLt3lIpWAsKOLZsOee+/g8AgU2bEDN2NF7VdO2YlC2VGxERKXW5p04TN2MWucdPgMlErb4PUeexRzDpuYBSDlRuRESkVCVv3MTRRW9hy8/HIyiQRqOfI6RNa2fHkipE5UZEREqFtaCAhCXLSP724jURgc2bETNmFF5hoU5OJlWNyo2IiNy03MSTF6ehEk+CyUTth/9M7b/8SdNQ4hQqNyIiclPObfiOhMVvY7NY8AgJJmbMKIJbXnvdJpGypHIjIiI3xJqXx9G3lpLy/UYAglq1JGbMc3gGBzs1l4jKjYiIOCzn+AniZswi79RpMJup8+jD1Or7kKahxCWo3IiIiN0Mw+DcN99y7O13sFkseIaGEjNuFEHNmjk7mkgxlRsREbFLUW4uRxcu5vyPmwEIbtuGmFHP4BEU5ORkIiWp3IiIyHVlJyQQ9/os8s+cBbOZuv0fp2bvBzCZXf75y1IFqdyIiMhVGYbB2a++5tiydzGKivCsVo3YcaMJbNLY2dFErqpUyk1RURHZ2dkE6wp5EZFKoygnhyMLFpG6ZSsAIe3b0ejZkXgEBjg5mci1OXw+saioiAULFrB+/XoAtm7dSqdOnejYsSMDBgwgIyOj1EOKiEj5yjp8hF2jx5G6ZSsmNzfqDR5Ik0nPq9hIheBwuXnjjTdYtGgRWVlZAEybNo2QkBBeeOEFEhMTmTVrVqmHFBGR8mEYBkmff8He5ydRcC4Zr4gIWrw6lZoP3o/JZHJ2PBG7OFxuvvjiC8aMGcPjjz9OQkIChw8fZvjw4fz1r39l9OjRfPfdd2WRU0REylhhVhaHpr/GsaUXr68Jva0DrefMJCCmkbOjiTjE4WtukpOTadWqFQCbNm3CbDbTpUsXAKKioorP6IiISMWRFRdP3IxZFKScx+TuTv3BA4j64706WyMVksPlJiIiglOnTtGuXTu++eYbmjRpQmjoxSe+/vrrr0RFRZV6SBERKRuGzUbSZ59zYuX7GFYr3lFRxI4fg3/DBs6OJnLDHC43DzzwANOnT+fzzz9n586dTJ48GYCpU6eyevVqhg0bVuohRUSk9BVmZnJ43gIu7NgJQNjtnWg4cjjuvr5OTiZycxwuN88++yze3t5s376dsWPH8thjjwGwd+9eBg8ezIgRI0o9pIiIlK7MAweJmzkHS2oqJg8Pop8cTOQ9d2kaSioFk2EYhrNDlKeePXsCsGHDBicnEREpf4bNxum16zjx/mqw2fCuUYPGE8biV7+es6OJXJMjP79vaBE/i8XCxx9/zJYtW0hJSWHatGls27aNZs2a0bJlyxvZpYiIlDFLegaH58wjfdduAMK7diF62FO4+/o4OZlI6XK43KSlpTFgwAASEhKIjo7myJEj5Ofns3HjRl599VWWL19OmzZtyiKriIjcoIy9+4ibNZfCCxcwe3oSPfRJInr20DSUVEoOr3Pz+uuvk5OTw5dffsmnn37KpVmt+fPn06JFC+bPn1/qIUVE5MYYViuJH6xh3+QpFF64gE+tWrSc+RqRd/ZUsZFKy+Fy8/333/Pcc89Rt27dEn8wvLy8GDx4MPv37y/VgCIicmMsFy6w/+VXOLn6Q7DZiOjRnVazXsOvbh1nRxMpUw5PSxUUFFz1AZlubm4UFhbebCYREblJ6bt2Ez97HoUZGZi9vGgw7CkienRzdiyRcuFwuWnRogWrVq2ia9eul732+eef07x581IJJiIijrs0DXXqo0/AMPCtW4fYCWPxrVXL2dFEyo3D5ea5555j4MCBPPjgg3Tt2hWTycQXX3zBG2+8wU8//cTSpUvLIqeIiFxHQWoq8bPmkrn/AACRd99J/ScH4+bl5eRkIuXL4Wtu2rVrx7vvvouPjw9Lly7FMAyWL19OSkoKb731FrfddltZ5BSpEqw2g71HzvPDL6fYe+Q8VluVWoZKbsKFX35l16hxZO4/gNnbm5ixo2j49HAVG6mSbmidm/bt2/PBBx+Qn59PRkYG/v7++Pn5lXY2kSply54klqzbS2pGfvG2sCBvnurdgk4tazgxmbgyW1ERie+v5vTadQD41a9P7IQx+NTQZ0aqLrvKTVJS0jVfz8jIICMjo/jrGvpDJeKQLXuSmL5i+2XbUzPymb5iOy8MaK+CI5cpSDlP3MzZZB2KAyDq3j9Qf/AAzJ6eTk4m4lx2lZsePRxb6OngwYM3HEikqrHaDJas23vNMW9/to8OzavjZta6JHJR2rbtHJ6/gKKsbNx8fWk4cgTVbu/o7FgiLsGucjNt2jQt9iRSRg4kpJaYirqS8+l5HEhIpUXDauWUSlyVrbCQEyvfJ+mzzwHwb9iA2PFj8I6KcnIyEddhV7np06dPWecQqbLSMq9dbBwdJ5VX/rlk4mbMJvvwYQCq39+LegP6Y/bwcHIyEddiV7lZt24dXbt2JSQkhHXr1l13fO/evW8ylkjVERroXarjpHJK3fozh994E2tODm5+fjR69mnCbuvg7FgiLsmucvP888+zZs0aQkJCeP7556851mQyqdyIOKBpdBhhQd7XnJqqFuxD0+iwckwlrsJWWMjxd//JmX99CYB/TKOL01AREU5OJuK67Co3GzZsIDw8vPjXIlJ63Mwmnurd4op3S10y5MHmupi4Cso7c5a4GbPJOXoUgBq9H6Bu/8cxu9/QKh4iVYZdf0Jq1qxZ/Ovt27cXT1H9XkpKCuvWrWPIkCGll1BEpAo6/9NmjixYhDUvD/eAABqNeobQdrc4O5ZIheDwCsUvvPACJ0+evOJrBw8eZP78+TcdSqQqsdoMFny065pj3v5sn1YrriJsFgtHF71F3IzZWPPyCGjSmNZzZ6nYiDjArjM3Q4cO5ciRIwAYhsHTTz+N5xUWiUpNTaVOnTqlm1Ckkpv13g6ycguvOUa3glcNeaeTiJsxi5xjxwGo9ac+1HnsEUxubs4NJlLB2F1uPvroIwA+/fRTmjZtSmhoaIkxZrOZwMBA3TYu4oCfdp3mx93XXgH8Et0KXrklb9zE0UVvYcvPxyMokEajniWkbRtnxxKpkOwqN23btqVt27bFX48YMYLatWuXWSiRqsBqM5i/5le7x+tW8MrJWlBAwpJlJH978WaNwObNiBkzCq+w0Ou8U0SuxuFL7qdPn14WOUSqnL1Hz5NXYLVrbICvh24Fr4RyT54ibsYsck8kgslE7b/8idoP/1nTUCI3yeFyk5aWxtSpU9m4cSN5eXkYRsmLHE0mEwcOHCi1gCKV1b4j5+0ee3/nBroVvJJJ/u57ji5+G1tBAR7BwcSMeY7gVi2dHUukUnC43Lz88sv88MMP9OrVi6ioKMxmh2+4EhHA3nufPN3N/OXOmDLNIuXHmpfH0beWkvL9RgCCWrYgZsxzeF5heQ0RuTEOl5sff/yRF198kYcffrgs8ohUGS0bVGPNt/HXHde3eyOdtakkco6fIG7GLPJOnQazmTqPPkytvg9pGkqklDlcbjw9PXUxsUgpaN6wGv6+HmRf4zZwb093Hr47thxTSVkwDINz32zg2NvLsFkseIaGEjN2FEHNmzk7mkil5PCc0l133cUXX3xRFllEqhQ3s4ln/tz6mmNGP9pGZ20quKLcPOJnz+Xom4uwWSwEt21D67kzVWxEypDDZ26aNm3K3LlzOXnyJK1atcLbu+TtqSaTiaeffrrUAopUZp1a1uCFAe1569M9pGUWFG8PC/Lmqd4t6NSyhhPTyc3KTjhG3IxZ5CedAbOZuv0eo+ZDD2LStYoiZcpk/P52p+to3LjxtXdoMnHw4MGbClWWevbsCegBoOI6rDaDfUfOs+foeUxcnK5q0aCazthUYIZhcPbfX3Ns2XKMwkI8w8KIHT+GwCbX/vtTRK7OkZ/fDp+5OXTokOOJHLBw4UK2bt3KypUri7cdPHiQqVOnsm/fPoKDg+nfvz9PPPFEmeYQKQ9b9iSxZN1eUjP+f/Xhb7cn6qxNBVaUk8ORNxeRunkrACHtb6HRs8/gERjg5GQiVUepnxvNysq64fcuX778sgdvXrhwgUGDBlGvXj0++eQTnnnmGebNm8cnn3xys1FFnGrLniSmr9heotgApGbkM33Fdrbsse+xDOI6sg4fYfeY8aRu3orJzY16gwfQZNILKjYi5czhMzcWi4Xly5ezbds2CgsLixfxMwyD3Nxcjhw5wu7dux3a57lz55g0aRI7d+6kfv36JV5bs2YNnp6evPzyy7i7u9OgQQNOnDjB22+/Td++fR2NL+ISrDaDJev2XnPM25/to0Pz6pqeqgAMw+DMF19yfPk/MYqK8IoIJ3bcGAJitT6RiDM4fObm9ddfZ/bs2SQnJ3P06FFOnz5NXl4ee/bs4eDBgwwdOtThEPv37ycoKIj169fTqlWrEq/t2LGD9u3b4+7+/z3stttu49ixY6Smpjp8LBFXcCAh9bIzNr936Ung4tqKsrM5NP11ji19B6OoiNAOt9J6zkwVGxEncvjMzX/+8x8GDhzI888/z1tvvcWBAweYN28e586do1+/fthsNodD9OjRgx49elzxtbNnzxITU/IviYiICACSkpIIC9PzdqTiOZ+RV6rjxDmy4uKJmzmbguQUTO7u1Bs0gOq97sVk0tk2EWdy+MxNWloaXbt2BSA2Npa9ey+eWo+MjOSpp57iyy+/LNWA+fn5eHp6ltjm5eUFQEFBwZXeIuLyMrMtpTpOypdhs3F63Xr2vvA3CpJT8I6KpOVr06hx3x9VbERcgMNnbgICArBYLv6FW69ePc6cOUN2djb+/v7FX5cmb2/v4uNdcqnU+Pr6luqxRMpLoJ/n9Qc5ME7KT2FmFofnvcGFHTsBCLu9Iw2fHo67n5+Tk4nIJQ6fuWnXrh0rV64kNzeXWrVq4ePjwzfffAPAr7/+ir+/f6kGjIqKIjk5ucS2S19HRkaW6rFEysvZtBy7xlUL9injJOKIzIOH2DVqLBd27MTk4UH0sKeIHT9WxUbExThcbkaOHMmuXbsYOnQo7u7uPPbYY0yePJk+ffowb9487rnnnlIN2L59e3bu3InVai3etnXrVurXr6/rbaRCstoM/r31+HXHVQv2pmm0PuOuwLDZOPXxWva++Hcsqal416hOy9enU/3eezQNJeKCHJ6Wio2N5auvviI+/uLTjMeOHYu/vz+//PILPXr04KmnnirVgH379mXp0qVMmjSJJ598kj179rBixQqmTJlSqscRKS8HElJLPGrhau7uUE+3gbsAS3oGh+fOJ/3XXQBU69KZBsOH4u6rs2oirsrhcgMQHh5OeHg4cPFxC8OGDSvVUL8VFhbG0qVLmTp1Kg899BDh4eFMmDCBhx56qMyOKVKW7L0DKipM15Q5W8a+/cTNnEPhhQuYPT2JfuoJIu7sqbM1Ii7O4XKzbt26647p3bv3DUS56NVXX71sW8uWLfnwww9veJ8irkR3Srk+w2rl1MdrSfxgDdhs+NSqReyEsfjVrePsaCJiB4fLzfPPP3/F7SaTCTc3N9zc3G6q3IhUdrpTyrVZLlwgfvY8MvZcXOYiokc3oocOwc3b28nJRMReDpebKz2NMzc3l507d7JkyRLefPPNUgkmUlnZeweU7pQqf+m79xA/ex6F6emYvbxoMGwIET26OzuWiDjI4XJTs2bNK25v1KgRhYWFvPLKK6xateqmg4lUVrH1QjGbwGZcfYzZdHGclA/DaiXxgzWc+ugTMAx869YhdvxYfGvXcnY0EbkBpfpU8JiYGPbv31+auxSpdOKOp12z2MDF4hN3PK18AlVxBalp7Pv7y5xa8zEYBpF33UnLGa+q2IhUYDd0t9SVWCwW1qxZo7VnRK4jLfPaD8x0dJzcuAu//Er8nPkUZWZi9vam4YhhhHft7OxYInKTHC43PXr0uOw2SJvNxoULFygoKGDixImlFk6kMgoNtO/CVHvHieMMq5UT76/m9CefAuBXvx6x48fiU7OGk5OJSGlwuNzceuutV1zjwd/fn+7du9OpU6dSCSZSWTWNDsPT3YylyHbVMdWCfbQ6cRkpSDlP3Kw5ZB08BEDUvX+g/uABmD11d5pIZeFwubnSOjQiYr/x83+4ZrEBGPJgc61OXAbStu/g8Lw3KMrKxs3Xl4Yjh1Ptdv2DTKSyuaFrbg4ePMjp06ex2WxUr16d5s2bF5/N2bp1K35+frRs2bJUg4pUBht3nOTwyYzrjmvTWA+FLU22wkJOvLeKpHXrAfBr0IDY8WPwqR7l5GQiUhYcKjeffPIJCxcuJCkpCcO4eLuHyWQiLCyM4cOH8/DDDzNlyhQeeeQRlRuR37HaDBZ8vMuuscs/38fwvq3KNlAVkX8umbiZs8mOPwxA9fv+SL2Bf8Xs4eHkZCJSVuwuN6+++irLly+ndevW/PWvf6Vu3bqYTCZOnTrFv//97+L1bSwWCw8//HBZZhapkA4kpFJQeO3pqEuSzueUcZqqIfW/P3N4/ptYc3Jw8/Oj0bNPE3ZbB2fHEpEyZle52bx5M8uXL+f5559n4MCBl73++OOPs2DBAt58800mTpyIj49WVhX5PUdu7a5Rza8Mk1R+tsJCji//J2e++BIA/5hGxI4bg3dkhJOTiUh5sGsRv5UrV3L33Xdfsdhc8tNPP+Hl5cWWLVtKK5tIpeLIrd0D729ehkkqt7wzZ9kzcVJxsanR+wFaTHtFxUakCrGr3Ozfv5/77rvvmq/v27ePF154gQMHDpRaOJHKpGl0GGFB1y84tzaNxMfTrRwSVT7nN29h95jx5Bw9inuAP03+9gL1Bw3Q9TUiVYxd01JZWVmEhl79OTfNmjXjm2++4fTp02RnZ5daOJHKxM1somubmqzdePSqY6qH+fL3J24rx1SVg81i4dg7yzn71dcABDRpTOzY0XiFV3NyMhFxBrvO3ERGRpKQkHDNMdWrV+fo0aNERuoWVpErsdoMvtmWeM0x2XlFWK/34CkpIe90EnsmvFBcbGr9qQ/N/zFFxUakCrOr3HTr1o1//vOfWCyWq46xWCysXLmSbt26lVY2kUpl79HzZOUWXnNMVq6FvUfPl1Oiii/lhx/ZNWY8OceO4x4YSNOX/kbd/o9jdi+1x+aJSAVkV7kZOHAgycnJjBgxgnPnzl32+pkzZxg2bBgpKSkMGjSo1EOKVAZfbTlm17h9R1RursdaUMCRBYuInz0XW34+gc2b0XruLELatnF2NBFxAXb986Z69erMmzeP0aNH07NnT5o3b07NmjUBOHXqFPv378ff35/58+cTFaUVP0V+z2oz+OXQ5f8wuBKboWmpa8k9eYq4GbPIPZEIJhO1/tyXOo/8BZObLsIWkYvsPnfbsWNHvvjiC1auXMn333/Pxo0bAahRowZPPPEE/fv3p1o1zXGLXMmBhFTyLfYt4Bfgqwc4Xk3yd99zdPHb2AoK8AgOJmb0swS31krOIlKSQxPT1apVY/To0YwePbqs8ohUSo4s4Bfs71WGSSoma34+CW+9TfJ3GwEIatmCmDHP4RkS4txgIuKSdNWdSDkICrC/sFQL1grfv5VzIpG412eRd+oUmM3UeeQv1PpTH01DichVqdyIlANLodWucYF+njSNDivjNBWDYRgkf7uBhCXLsFkseISEEDtuNEHNmzk7moi4OJUbkXKw8kv7Vu4ODfTCzWwq4zSuryg3j4TFS0j5YRMAwW1a02jUs3gGBzk5mYhUBCo3ImXMajM4fibLrrFubnatzlCpZSccI27GbPKTksBspu7jj1KzT29MZn1vRMQ+KjciZezX+GS7x8bUDi67IC7OMAzO/vtrji1bjlFYiGdYGLHjxxDYpLGzo4lIBXND5SYtLY1ly5axZcsWUlJSWLp0Kd9++y2NGzfmzjvvLO2MIhXamx/tsnvsoAdalF0QF1aUk8ORNxeTunkLACHtb6HRs8/gERjg5GQiUhE5fJ735MmTPPDAA6xZs4bIyEhSU1OxWq0cO3aMZ599tnj9GxEBS5GN8+n23Qbu5WGukk8Dzz5ylN1jxpO6eQsmNzfqDRpAk0kvqNiIyA1z+MzNa6+9RlhYGCtXrsTX15fmzZsDMGvWLAoKCli8eLGeLyXyf77abN8jFwBqhvuVYRLXYxgGZ/71FcffXYFRVIRXRDix48YQEBvj7GgiUsE5fOZm69atjBgxgsDAQEymknd1PPzwwxw+fLjUwolUdGdSc+we2zo2ogyTuJai7GwOvTqDY28vwygqIrTDrbSeM1PFRkRKxQ1dc+N2lcWzLBbLZYVHpCqLDPW1e2zbmMgyTOI6suLiiZs5h4LkZEzu7tQb+Feq3/dH/d0hIqXG4XLTrl07lixZQqdOnfDyurjqqslkwmazsXr1atq2bVvqIUUqqtqR9l034uPlTvOGlfvZbIZhkLT+c06seA/DasU7KpKYcWMIaNTQ2dFEpJJxuNyMHTuWRx99lLvvvpsOHTpgMplYtmwZR48e5cSJE6xataoscopUSF//97hd41o3Cq/Ui/cVZmZxeP4bXNi+E4CwTh1pOHI47n5V6zojESkfDl9zExMTw8cff0yHDh34+eefcXNzY8uWLdSpU4cPPviAJk2alEVOkQrHajPYtv+sXWNzCwrLOI3zZB48xK7R47iwfScmDw+ihw0hdsJYFRsRKTM3dM1N/fr1mTVrVmlnEalU9h49j82wb2yBpahswziBYbNxeu06Try/Gmw2vGtUJ3b8WPyj6zs7mohUcnaVm+3btzu00/bt299QGJHKZN+R83aPjQytXGcxCjMyiJ/7Bum//ApAtS6daTB8KO6+euK5iJQ9u8pN//79L7uTwTBK/pPUZDJhGAYmk4mDBw+WXkKRCsrOkzYA9GxXp8xylLeM/fuJnzkXS1oaZk9P6g95gsi7eupuKBEpN3aVm3/+859lnUOk0omtG2LXOE93My1jwss4TdkzrFZOffIpias/BJsNn1o1iR0/Fr96dZ0dTUSqGLvKza233lrWOUQqne12XkzcNDqswt8pZUlPJ37WXDL27AUgokc3oocOwc3b28nJRKQquqELig8cOMCSJUvYsWMHmZmZhIWF0bFjR4YPH07t2rVLO6NIhbR1b5Jd47JyLGWcpGyl795D/Ox5FKanY/byosGwIUT06O7sWCJShTlcbrZu3cqQIUMIDg6ma9euhIWFcf78eTZt2sTXX3/N6tWriYnREupStVmKbGTk2Hd7d14FvQ3csFo5+eFHnFzzMRgGvnVqEzt+LL519A8cEXEuh8vNnDlzaNeuHW+99VbxCsUA+fn5PPnkk7z++ussXbq0VEOKVDTrNx2xe2xFfGBmQWoa8bPnkrlvPwCRd91J/SGDcfvN3wkiIs7i8CJ+8fHxDBo0qESxAfD29uaJJ55g586dpRZOpKL68D9xdo9t0bBiPTDzwi+/snv0WDL37cfs7U3MmFE0HDlcxUZEXIbDZ26qV6/OqVOnrvhaWloaoaGhNx1KpCLLs1jJL7TZPb7XHdFlmKb0GFYrias+4NTHawHwq1+P2PFj8alZw8nJRERKcrjcTJw4kYkTJxIQEECvXr2KnxD+008/MXfuXF5++eXSzihSoSz7bK/dYz3cTHi6O3wCtdwVpJwnbtYcsg4eAiDq3nuoP3ggZk9PJycTEbmcw+Xmf/7nf7BYLEycOJEXX3yRkJAQMjIyKCwsxDAMRo4cWTzWZDJx4MCBUg0s4up+3nfG7rGxdYPLLkgpSduxk8Nz51OUlY2bjw8NRw6n2h23OzuWiMhVOVxu+vTpUxY5RCoFq80gPdv+W7v/1D22DNPcHFtRESdWvk/SuvUA+DVoQOz4MfhUj3JyMhGRa3O43Pz2zIyIlPRrfLJD41s3ds2LifOTk4mbMZvs+MMAVO/1R+oN+itmDw8nJxMRub4bWsTPYrGQkJBAVlbWFV/XgzOlqvpso/23gPt4ml1yZeLU//7M4flvYs3Jwc3Pj0bPPE1Yxw7OjiUiYrcbWsRv7NixXLhwAfj/B2iW5YMzT58+TY8ePS7b/o9//IM///nPpXoskZuRlWf/gnwP9WxUhkkcZyss5PiKlZz5/F8A+DdqROz4MXhHuubZJRGRq3G43EybNo2QkBBefvllgoODyyDS5eLi4vDy8uLbb78t8WThgICAcjm+iL0a1Azi6KkMu8b27eY6K3nnnz17cRrqyFEAavR+gLr9HtM0lIhUSA6Xm8TERObMmXPFMyllJT4+nvr16xMRoX9BimsLC7HvQZGN6wW7zC3g5zdv5ciChVhzc3EP8KfRsyMJvVVTyyJScTlcbmJjY4unpMpLXFwcDRs2LNdjityIn36172GZOXlFZZzk+mwWC8feWcHZr/4NQEDjWGLHjcYrPNzJyUREbo7D5ebFF19k3LhxmM1mWrZsiY+Pz2VjatQo3RVL4+PjCQ8P57HHHuP48ePUrVuXESNG0Llz51I9jsjNOnku265xqRl5ZZzk2vKSkoh7fTY5x44BULPvQ9R57BHM7jd0j4GIiEu54bulXnzxxau+XpoXFFssFo4fP46Pjw8TJkzA19eX9evXM2TIEN599106duxYascSuRlZufZfTOzMCamUTT9y5M3F2PLzcQ8MJGb0s4S0bePERCIipcvhcvPyyy/j5ubG6NGjCS+H09eenp5s374dd3d3PP9vqffmzZtz9OhRli1bpnIjLuOlJZvtHluvRmAZJrkya0EBx5a+w7n/fAtAYLOmxIwdhVdYWLlnEREpSw6Xm4SEBObNm0f37t3LIs8V+fr6XrYtJiaGn376qdwyiFzPkZP23SUF5b8yce6pU8S9PovcE4lgMlHrz32p88hfMP3fs+FERCoTh8+O161bl7y88rte4NChQ7Rp04YdO3aU2L5v3z5dZCwuw2ozMBwYX54rEyd/t5HdYyaQeyIRj+Bgmr38d+o+/qiKjYhUWg6fuXnuued47bXXCAoKonXr1vj5+ZVFrmIxMTE0atSIKVOm8NJLLxESEsKaNWvYtWsXH3/8cZkeW8Re2/afdWh8eaxMbM3PJ+GtpSR/9z0AQS1bEDPmOTxDQsr82CIizuRwuZk1axbnz5/nySefvOLrpf0kcLPZzOLFi5k5cyajRo0iMzOTpk2b8u677xIb67oPHZSqZd4Hv9g9tlpg2S+Ml5uYyKHXZ5F38hSYzdR++M/U/nNfna0RkSrB4XLTq1evsshxTaGhoUybNq3cjytiD6vNICff/nVr2jWtXmZZDMMg+dsNJCxZhs1iwSMkhNixowhq0bzMjiki4mr0VHCRm7Tj4DmHxg9+sGWZ5CjKzSNh8RJSftgEQHDrVjQa/RyewUFlcjwREVd1Q+vc5OfnExcXR2FhYfGDM202G3l5eezYsYNx48aVakgRV7bss30OjffxLP2poZxjxzn0+izyk5LAbKbu449Ss09vTGbXeMSDiEh5crjc/Pe//+W5554jMzPziq/7+fmp3EiVcjY1x+6xDWqU7gX4hmFw7utvSFj6DkZhIZ5hYcSOG01g0yalehwRkYrE4XIzd+5cgoOD+cc//sH69esxm8306dOHTZs2sXr1at5+++2yyCnikvIsVoduAX9leNdSO3ZRbi5HFiwidfMWAELa3UKj50biEVj+CwSKiLgSh8tNXFwcr7zyCnfddRfZ2dmsWrWKrl270rVrVwoLC1m0aBFLliwpi6wiLmfJ2t0OjQ/wLZ07pbKPHCVuxmzyz57F5OZG3f6PU+PB+zUNJSLCDZQbm81GVFQUAPXr1+fIkSPFr91zzz1MnDix9NKJuLiNv5ws1+MZhsGZf33F8XdXYBQV4RURTuy4MQTExpRrDhERV+bwP/Pq1KlDXFwc8P+rFR89ehSAoqIicnLsv/5ApCKz2gyKrPaP9/O6ueMVZecQ99oMjr29DKOoiNAO7Wk9Z6aKjYjI7zh85ub+++9n5syZ2Gw2+vfvT/PmzfnHP/5B//79Wbx4sR6JIFWGo6sSvznhnhs+Vlb8YeJmzKYgORmTuzv1Bvan+n29MJnKfqVjEZGKxuFy8+STT3LhwgX27NkDwEsvvcSQIUMYMWIE/v7+LFq0qNRDiriihR/vcmh8WLC3w8cwDIOk9Z9zYsV7GFYrXpERxI4fS0Aj/SNCRORqHC43ZrO5xHU1LVq04NtvvyUhIYHo6Gj8/f1LNaCIq0rPttg91v0GTrAUZmVxeN4CLmy/+NDYsE4daThyOO5l/Dw3EZGK7oYW8futjIwMEhMTqV+/voqNVBmOPG4BYMmLdzs0PvPgIeJmzsFy/jwmd3fqPzGIqHvv0TSUiIgd7L6geM+ePQwbNox169YVb1u5ciVdunThL3/5C507d2bZsmVlkVHE5bz+z20OjQ8P9bFrnGGzcWrtOva++Hcs58/jXT2KljOmU/2Pf1CxERGxk11nbg4ePEi/fv0IDQ2lT58+wMWyM23aNBo2bMhzzz1HQkICc+bMoW7dutx5551lGlrE2XbHp5T6PgszMjg87w0u7PwVgGpd7qDB8GG4+9pXjERE5CK7ys2SJUto0qQJy5cvx8fn4l+0K1euBGDGjBk0btwYgPPnz7Ny5UqVG6n0rA4sS1yz2vXLScb+/cTPnIslLQ2zpyf1hwwm8q47dbZGROQG2FVutm/fzvPPP19cbAB++uknateuXVxsAO644w4+/fTT0k8p4kIuZBY4NP61Z7pd9TXDauXUJ5+SuPpDsNnwqVWT2PFj8atX9yZTiohUXXaVm/T09OJViQGOHj3KhQsXLjtD4+Pjg8Vi/x0kIhXR6DnfOzQ+yN/zitst6enEz55Hxu6LyyqEd+9Gg6FP4uajaSgRkZthV7kJDg7m/PnzxV//97//xWQy0bFjxxLjjh49SmhoaOkmFHExqQ6eubmS9D17iZ89l8IL6Zi9vIge+iSRPXuUQjoREbGr3Nx66618+OGH3H333dhsNj755BO8vLzo3Llz8RiLxcL7779P27ZtyyysiLNZbY48Axwigkr+ETOsVk5++BEn13wMhoFvndrEjh+Lb53apRlTRKRKs6vcDB8+nIcffrh4GiopKYmnn36agIAAAD755BPef/99jh07xuuvv152aUWczNFHLswec1fxrwtS04ifPZfMffsBiLizJ9FPPYGb100+dEpEREqwq9w0atSINWvW8M4775CamsqQIUN49NFHi1+fO3cu7u7uvPnmmzRp0qTMwoo428JPdjk0/tL1Nhd+3cXhOfMozMjE7O1Ng+FDiejWpQwSioiI3SsUN2zYkGnTpl3xtY8//pjw8HDMZocfMi5SoaRn2X/BvJmL01CJqz7g1CefXpyGqlf34jRUrZplF1JEpIq76ccvAERGRpbGbkRcWp7F6tD4t55ux76/vUTmgYMARP3hbuoNHqhpKBGRMlYq5UakKpjz3g67x0bnnOLE5E8pysrCzceHhiOHU+2O28swnYiIXKJyI2KnrXZcTGw2bHRJ/ZXb0vdTBPg1iCZ2/Bh8qlcv+4AiIgKo3IjYJSu38LpjAguzefDsJmoWXFwTqnqvP1Jv0F8xe3iUdTwREfkNlRsROzz/xqZrvt4oO5E/Jm/Bx2Yh3+xB/eEjiL5bd0OJiDiDyo2IHRKTs6+43WxY6X7+F9pnXLxoOMkrjM+iuvCeio2IiNOo3Ihcx7HEjCtuDyrMovfZTVQvSAVgW3BTNoa1AZNbecYTEZHfUbkRuY5n5228bFts9gnuTd6Ct62QPLMn/4q8nSN+Fx+h8M+X/lDOCUVE5LdUbkSu4fdr27jZrPRI3cEtGXEAnPIOZ31kZzI9/IvHhARqHRsREWdSuRG5hr+88EXxr0MsmTx4bhNRBWkAbA1uxo9hbbCZtDK3iIgrUbkRuYqc/KLiXzfJOsYfkv+Ll1FIrtmLLyLvIMHv8kcoLBjdvTwjiojIFajciFzFI5P+hbutiJ7nt9Mm8zAAJ70j+CyqC9nuvld8T91ageUZUURErkDlRuQKziTnEGrJoPfZH4iwpGMAW0Ja8FNoK4yrTEO9PvSO8g0pIiJXpHIjcgXzJr3FwJSf8TSKyHHz5vPIOzjuW+Oa72kSE1ZO6URE5FpUbkR+w5qfz5InJ3N/1lEATvhEsT7yDnKuMg11yfN/vqU84omIiB1UbkT+T25iIv8Z+zItLRnYMLE5tCVbQlpcdRrqt26/rVY5JBQREXuo3EiVZxgGyRu+4+CCtwg3rGS7+bA+sjOJvlF2vf+vf4gp44QiIuIIlRup0qx5eRxdvISUjZvwABJ8qvNF5B3kuvvYvY8/39Wk7AKKiIjDVG6kyso5fpwfJ04hID8TGyZ+DG3N1pDmYDLZvY9Xh9xehglFRORGqNxIlWMYBvtXryd1zXsEGDYy3XxZH9WZUz6RDu+rWeNqZZBQRERuhsqNVClFubm8O+TvNM0+jjtwxLcm/4q8nTw3b4f39fmsB0s/oIiI3DSVG6kynnn6He4+t4mmhVlYMfFDWFu2BTd1aBrqEhUbERHXpXIjld7DY9bROCOOP53fgTs2Mtz9+CyyC0k+4Te0PxUbERHXpnIjldawv3/G+SwLf0zeQmxOIgCH/Wrxr4jbyXfzuqF9qtiIiLg+lRupdF6c8xl7T0H1/PMMOruJ4KJsrJj5vtot7AhqfEPTUKBiIyJSUajcSKXxt3mfsTsRMAzaZxyk2/lfcMNGurs/66K6cNb7xu9sUrEREak4VG6kwnts7Gdk/d+vva0F9EreTKOcUwAc8qvDVxGdKHDzvOH9q9iIiFQsKjdSYd0/9rMSX9fIS+HBcz8QVJRLEWa+q9aOX4Jib3gaClRsREQqogpRbmw2GwsWLOCjjz4iMzOTW265hZdeeom6des6O5o4we9LDYZBh/T9dE39FTMGaR4BfBbVhXNeYTd8jKUv3EVktWs/CVxERFxThSg3Cxcu5IMPPmD69OlERkYyY8YMhgwZwhdffIGn541PN0jFclmpAXys+dx3bjMNck8DcMC/Hv+OuA2L+cY+F7X8YNH/6GyNiEhF5vLlxmKx8M477zB+/Hi6du0KwJw5c+jcuTPffPMNvXr1cnJCKWtXKjUAtfLO8cDZHwm05lJocuPbau3ZHdhId0OJiFRxLl9uDh06RE5ODrfddlvxtsDAQJo2bcr27dtVbiqxq5UaDIOOF/bSOW03ZgxSPQJZF9WVFK+QGzpOy9owdZSKjYhIZeHy5ebs2bMAVK9evcT2iIgIzpw544xIUsauWmoA36I87j/3E/XzLv7e7wuI5uvwDhSaPW7oWDpbIyJS+bh8ucnLywO47NoaLy8vMjIynBFJysi1Sg1A3dwz3H/uJ/yteRSa3PhPeAf2BjTQs6FERKQEly833t4Xn9ZssViKfw1QUFCAj4+Ps2JJKbEU2eg78fNrjjEZNm5P28PtF/ZgAlI8g1gX1ZVUz2CHj6dSIyJS+bl8ubk0HZWcnEydOnWKtycnJ9O4cWNnxZJScL0zNQB+Rbk8cO4n6uZdnJ7cHdCQb8Jvpcjs2EdXpUZEpOpw+XLTuHFj/P39+fnnn4vLTWZmJgcOHKBfv35OTic3wp5SA1AvN4n7z/2EnzUfi8mdryNuY39AtEPHUqkREal6XL7ceHp60q9fP2bOnEloaCg1a9ZkxowZREVFcddddzk7njjA3lJjMmzckbabThf2YgKSPUNYF9WFNM8gu4+lUiMiUnW5fLkBePbZZykqKuJvf/sb+fn5tG/fnmXLlmkBvwrC3lIDEFCUwwNnf6R2fjIAvwbGsKFaO7unoVRqRESkQpQbNzc3xo8fz/jx450dRRww9/0f2PBLut3jo3NOcd+5zfjaCigwefDviNs4GFDfrveq1IiIyCUVotxIxbL3QAovLtti93izYaNL6q/clr4fgLNeoayL7EK6Z+B136tSIyIiv6dyI6XKkSkogMDCbB449yO18lMA2BkUy3fV2mE1uV31PdEhMO9vKjUiInJlKjdSKnbuOsvLK3926D0Nc07S69xmfGwW8s0efBXRiTj/qz/p/bG7G/LoPc1uNqqIiFRyKjdy0xw9W2M2rHQ//wvtMw4CkOQVxmdRXcjwCLji+JnDOxPbMPSmc4qISNWgciM37PTZbIbN2ODQe4IKs3jw7CZqFKQCsC2oCRurtcV2hWkoXU8jIiI3QuVGboijZ2sAYrJP8MfkLXjbCskze/KvyNs54lf7snEqNSIicjNUbsQhGdkW+r30lUPvcbNZ6ZG6g1sy4gA45R3O+sjOZHr4lxg395muNKgXXFpRRUSkilK5Ebs9+rcvyc4rdOg9wZZMep/bRFRBGgD/DW7GprA22Ezm4jGvD72DJjFhpZpVRESqLpUbscuNTEM1zjrGvcn/xcsoJNfsxReRt5PgV6v49e6tAxnTv3tpxhQREVG5ketztNi424roeX4HbTLjATjpHcH6qM5kufsB0K1VAGP/2qPUc4qIiIDKjVyHo8Um1JJB77ObiLBcwAC2hLTgp9BWGCYz/sBqXSwsIiJlTOVGrsrRYtMsK4F7kv+Lp1FEjps3n0fewXHfGoDugBIRkfKjciNX5EixcbcVcVfKNlplHQHghE8U6yPvIMfdlxEPNOferg3KKqaIiMhlVG7kMo4Um2oF6Tx47gfCLRkYwE+hrdgS0gLDZNbZGhERcQqVGynB7mJjGLTIOsrdKT/jYVjJdvNhfWRnEn2jGHhvLH3vbFy2QUVERK5C5UaK2VtsPGyF3JPyM82zEgA45lOdzyPvINfdR2drRETE6VRuBLC/2IQXXKD32R8IK8zEhokfQ1uzNaQ5f3vsVjq0q1HGKUVERK5P5UbsKzaGQavMw9x5fjsehpVMN1/WR3XmlE+kztaIiIhLUbmp4uwpNp42C39I/i9Ns48DcNS3Jl9E3k6em7eKjYiIuByVmyps8cc7rzsmsiCVB89uIrQwCxsmNoa1YVtwMzCZVGxERMQlqdxUUZYiG//aeurqAwyDthlx9Di/A3dsZLj78VlkF5J8wgEtyiciIq5L5aaKevKVf1/1NS+rhXuTt9I45wQAh/1q8a+I28l38wJUbERExLWp3FRBOflFXMguvOJrUfnn6X12E8FF2Vgx8321tuwIagImE6BiIyIirk/lpgp6auoVztoYBu0yDtL9/C+4YSPd3Z91UV04612teIiKjYiIVAQqN1VMnsVKZq61xDZvawG9kjfTKOfiNThxfnX4MqITBW6exWNUbEREpKJQualiBk/5ssTXNfJSePDcJoKKcijCzHfV2vFLUGzxNBSo2IiISMWiclOF5OQXkZ1vu/iFYXBr+n66pv6KGwYXPAJYF9mFc95hJd7zz5f+4ISkIiIiN07lpgp5atp/APCx5tPr3GYa5p4G4IB/Pf4dcRsWs2eJ8T5e7oQEepV7ThERkZuhclNF5FmsZOYUUivvHA+c/ZFAay5FJjPfVLuV3YGNSkxDXbJmWi8nJBUREbk5KjdVxIQ539MxbS+d03ZhxiDVI5B1UV1J8Qq54vgX+7cv54QiIiKlQ+WmCshJvcCtv64jOu8MAPsCovk6vAOFZo+rvufWltXLK56IiEipUrmp5DL27uPnl18juiiXQpMb/wnvwN6ABlechrrkLz1jcDNf/XURERFXpnJTSRlWKyc/+oTE1WvwwyDFM4jPIrty3iv4uu997A+Nyz6giIhIGVG5qYQsaReInzOPjD17MQF7AhrwTfit15yGumTMY2111kZERCo0lZtKJn3XbuJnz6MwIwOLyZ2vwzuwP7CBXe/19DDT/ZbaZZxQRESkbKncVBKG1Uri6g859fFaMAwu+IbyUbXOpHkG2b2Pf758bxkmFBERKR8qN5VAQWoq8TPnkHngIADWth1Zll6fIrP9v72Bvh74eevjICIiFZ9+mlVwF3b+QvzcNyjKzMTs7U30iGE89UUaNrNj+1n693vKJqCIiEg5U7mpoGxFRSS+v5rTa9cB4Bddn9jxY/h0TwY2W5pD+2rfNBIfT7cySCkiIlL+VG4qoIKUFOJmziHrUBwAUX/8A/UHDcBw92DN3B0O7Ss00IvJT9xWFjFFREScQuWmgknbtp3D8xZQlJ2Nm58vDUeOoFqnjgCs+vdBrFbD7n2ZTfCOpqNERKSSUbmpIGyFhZxY+T5Jn30OgH+jhsSOG413VBQAVpvBx98fdmifE//aXmvaiIhIpaNyUwHknztH3Iw5ZB++WF6q338f9Qb0w+zx/4vyrfk2jsIi+8/aTPxrOzq1rFHqWUVERJxN5cbFpW79L4ffeBNrTi5ufn40em4kYR1uLTHGajNY+/0Ru/f58J0x3NGqZmlHFRERcQkqNy7KVljI8XdXcOZfXwEQEBtDzLjReEdEXDZ279Hz5Fusdu3X08PMo/fo2VEiIlJ5qdy4oLwzZ4ibMZucowkA1HzoQer0ewyz+5V/u/YdOW/3vkc/qmdHiYhI5aZy42LO/7SZIwsWYc3Lwz0ggEajniG03S3XfI+9V9rE1g3WdJSIiFR6KjcuwlpQwLFlyzn39X8ACGzahJixo/GqFnbd97ZsUI0138Zfd1z/PzS96ZwiIiKuTuXGBeSeOk3cjFnkHj8BJhO1+j5EncceweRm36rBWbmW644J8PWkecNqNxtVRETE5VWIcrNt2zb69+9/2fZ3332XTp06OSFR6UneuImji97Clp+PR1AgjUY/R0ib1na/32ozWLp+33XHjfhTS11rIyIiVUKFKDdxcXHUqVOHVatWldgeFBTkpEQ3z1pQQMKSZSR/uwGAwObNiB07Gs/QEIf2cyAhldSM/OuOC/LzuqGcIiIiFU2FKDfx8fE0atSI8PBwZ0cpFbmJJy9OQyWeBJOJ2g//mdp/+ZPd01C/9d99Z+wal5Z5/QIkIiJSGVSIchMXF8dtt1WOhzue2/AdCW8txVZQgEdIMDFjRhHcssUN7WvLniTW/5hg19jQQO8bOoaIiEhF4/LlxjAMDh8+THh4OH369OHcuXPExMQwevRoWrZs6ex4drPm5XH0raWkfL8RgKBWLYkZ8xyewcE3tj+bwZJ1e+0aWy3Yh6bR17/rSkREpDJwerk5deoUPXv2vOrrH3zwAbm5uVgsFiZPnozJZOKf//wn/fr1Y+3atTRs2LAc096YnOMniJsxi7xTp8Fsps6jD1PrT30wmc03vE97r7UBGPJgc11MLCIiVYbTy01kZCRffvnlVV+vV68eO3bswNfXF7f/uyZlxowZ3HfffaxcuZIpU6aUV1SHGYbBuW++5djb72CzWPAMDSVm3CiCmjW7qf1abQa7D6fYNfaBztF6QKaIiFQpTi83Hh4eNGjQ4JpjAgICSnxtNptp2LAh586dK8toN6UoN5ejCxdz/sfNAAS3bUPMqGfwuMk7vLbsSWLJur12n7W5rXn1mzqeiIhIRXPj8yLlZOPGjbRu3ZozZ/7/rqCioiIOHTrkslNS2QkJ7B4z/mKxMZupO6A/Tf/+YqkUm+krtttdbKoFe+taGxERqXKcfubmetq1a0dYWBgTJkzg+eefx93dnSVLlpCens7AgQOdHa8EwzA4+9XXHFv2LkZREZ7VqhE7bjSBTW7+KdyOXEB8yd0d6ulaGxERqXJcvtz4+/uzfPlyZsyYweDBgykoKOCWW27hvffeo1o113mcQFFODkcWLCJ1y1YAQtq3o9FzI/H43ZTajXLkAuJLalTzK5Vji4iIVCQuX24Aateuzfz5850d46qyDh8hbsYsCs4lY3J3p+5f+1HjgfswmUrvrMmNLMKntW1ERKQqqhDlxlUZhsGZL/7F8eUrMYqK8IqIIHb8GAJiGpX6sRwtKlrbRkREqiqVmxtUlJ3N4flvkvbzNgDCOnag4cincfcvm6mgptFhhAV5a20bERGR63D5u6VcUVZcPLtGjyPt522Y3N2JfuoJYieOL7NiA+BmNvFU7+s/pqFasA8vDGivtW1ERKTK0pkbBxg2G0mffc6Jle9jWK14R0URO34M/g2vvU5PaenUsgYvDGh/2To3gX6edGtbi9uaV6dpdJjO2IiISJWmcmOnwsxMDs9bwIUdOwEIu70TDUcOx93Xt1xzdGpZgw7Nq3MgIZW0zHxCA71VaERERH5D5cYOmQcOEjdzDpbUVEweHkQ/OZjIe+4q1buhHOFmNtGioevcBi8iIuJKVG6uwbDZOL12HSfeXw02G941atB4wlj86tdzdjQRERG5CpWbq7CkZ3B4zjzSd+0GILxbFxoMewo3Hx8nJxMREZFrUbm5goy9+4ibNZfCCxcwe3oSPfRJInr2cNo0lIiIiNhP5eY3DKuVkx99wskPPwKbDZ9atWg8cSy+deo4O5qIiIjYSeXm/1guXCB+9jwy9lx8OGVEzx5EP/UEbt56hIGIiEhFonIDpO/aTfyc+RSmp2P28qLB8KeI6N7N2bFERETkBlTpcmNYrSR+sIZTH30ChoFv3TrEThiLb61azo4mIiIiN6jKlpuC1FTiZ80lc/8BACLvvpP6Tw7GzcvLyclERETkZlTJcmOzWNg1ahxFmZmYvb1p+PQwwrt0dnYsERERKQUmwzAMZ4coTy1atKDIYiHUwwOzuwcewUGY3NycHUtERESu4cyZM7i5ubF3797rjq1yZ268vLwwmUx4h4c7O4qIiIjYyd3dHU9PT7vGVrkzNyIiIlK5mZ0dQERERKQ0qdyIiIhIpaJyIyIiIpWKyo2IiIhUKio3IiIiUqmo3IiIiEilonIjIiIilYrKjYiIiFQqKjciIiJSqajciIiISKWiciMiIiKVisqNiIiIVCoqN06wbds2YmNjL/tvy5Ytzo7m0mw2G/Pnz6dz5860atWKwYMHc+LECWfHqpBOnz59xc/gRx995OxoFcLChQvp379/iW0HDx6kX79+tG7dmm7durFs2TInpatYrvS9fOGFFy77bHbp0sVJCV1Xeno6kydPpkuXLrRt25ZHH32UHTt2FL9elT+T7s4OUBXFxcVRp04dVq1aVWJ7UFCQkxJVDAsXLuSDDz5g+vTpREZGMmPGDIYMGcIXX3yBp6ens+NVKHFxcXh5efHtt99iMpmKtwcEBDgxVcWwfPly5s+fT/v27Yu3XbhwgUGDBnHnnXcyZcoUdu3axZQpUwgODqZv375OTOvarvS9hIufz2HDhtGvX7/ibW5ubuUdz+WNGTOG1NRUZs+eTWhoKKtWreKJJ55g7dq1hIaGVunPpMqNE8THx9OoUSPCw8OdHaXCsFgsvPPOO4wfP56uXbsCMGfOHDp37sw333xDr169nJywYomPj6d+/fpEREQ4O0qFce7cOSZNmsTOnTupX79+idfWrFmDp6cnL7/8Mu7u7jRo0IATJ07w9ttvV4kfJI661vfSarVy5MgRRowYob8jr+HEiRNs3ryZ1atX07ZtWwAmTZrEpk2b+OKLL/D29q7Sn0lNSzlBXFwcDRs2dHaMCuXQoUPk5ORw2223FW8LDAykadOmbN++3YnJKiZ9Bh23f/9+goKCWL9+Pa1atSrx2o4dO2jfvj3u7v//78XbbruNY8eOkZqaWt5RXd61vpfHjx+noKCABg0aOCldxRASEsKSJUto3rx58TaTyYRhGGRkZFT5z6TKTTkzDIPDhw9z9OhR+vTpw+23386gQYPYs2ePs6O5tLNnzwJQvXr1EtsjIiI4c+aMMyJVaPHx8aSmpvLYY4/RqVMnHn30UX788Udnx3JpPXr0YNasWdSuXfuy186ePUtUVFSJbZfOiiUlJZVLvorkWt/L+Ph4TCYTK1asoEePHtx555288sorZGVlOSGp6woMDKRr164lpuS/+uorEhMTueOOO6r8Z1LlppSdOnXqihdqXvpv165d5ObmYrFYmDx5MgsXLiQ0NJR+/fpx5MgRZ8d3WXl5eQCXXVvj5eVFQUGBMyJVWBaLhePHj5Odnc2oUaNYsmQJLVq0YMiQIWzdutXZ8Sqk/Pz8K342AX0+HXT48GHMZjM1a9Zk8eLFTJw4kR9++IERI0Zgs9mcHc9l7dy5kxdffJGePXvSo0ePKv+Z1DU3pSwyMpIvv/zyqq/Xq1ePHTt24OvrW3yB3IwZM7jvvvtYuXIlU6ZMKa+oFYq3tzdw8QfzpV/DxT+kPj4+zopVIXl6erJ9+3bc3d2L//Jr3rw5R48eZdmyZXTs2NHJCSseb29vLBZLiW2XfoD4+vo6I1KF9cwzzzBw4EACAwMBiImJITw8nIcffpi9e/deNo0l8O233zJu3DhatWrF7NmzAX0mVW5KmYeHx3Xnin9/R4rZbKZhw4acO3euLKNVaJemo5KTk6lTp07x9uTkZBo3buysWBXWlf5yi4mJ4aeffnJCmoovKiqK5OTkEtsufR0ZGemMSBWWyWQqLjaXxMTEABen/1RuSnrvvfeYOnUqd911FzNnziz+B0tV/0xqWqqcbdy4kdatW5e4TqSoqIhDhw7pAs9raNy4Mf7+/vz888/F2zIzMzlw4ADt2rVzYrKK59ChQ7Rp06bEehgA+/bt02fwBrVv356dO3ditVqLt23dupX69esTFhbmxGQVz9ixY3niiSdKbNu7dy+APp+/s2rVKl555RUef/xx5s6dW2Iaqqp/JlVuylm7du0ICwtjwoQJ7N+/n7i4OCZOnEh6ejoDBw50djyX5enpSb9+/Zg5cyYbNmzg0KFDjB49mqioKO666y5nx6tQYmJiaNSoEVOmTGHHjh0cPXqU6dOns2vXLoYNG+bseBVS3759yc7OZtKkSRw5coS1a9eyYsUKhg4d6uxoFc59993H5s2bWbRoEYmJifzwww+8+OKL3HfffbqD6jeOHTvGtGnTuOuuuxg6dCipqamkpKSQkpJCVlZWlf9MmgzDMJwdoqo5efIkM2bM4Oeff6agoIBbbrmFiRMnFp96lSuzWq3Mnj2btWvXkp+fT/v27Zk8eTK1atVydrQKJy0tjZkzZ7Jp0yYyMzNp2rQp48aN01kwOz3//POcPn2alStXFm/bs2cPU6dO5cCBA4SHhzN48OASi9DJlV3pe/n111+zePFiEhISCAgI4P7772fUqFHFF8QKLF68mDlz5lzxtYceeohXX321Sn8mVW5ERESkUtG0lIiIiFQqKjciIiJSqajciIiISKWiciMiIiKVisqNiIiIVCoqNyIiIlKpqNyIiIhIpaJyIyLlSktr2U/fK5Ebo3Ij4oKef/55YmNjr/lfjx49isde+rWr++ijj3jttdeKv167di2xsbGcOnWq1I5x6tQpYmNjWbt27XXHHjlyhL///e/07NmTli1b0q1bN8aMGcPu3btLLc+N2rBhAxMnTiz++ueffyY2Nrb4+WpvvPEGsbGxzoon4tL0VHARFzRixAgeeeSR4q8XLlzIgQMHWLBgQfG23z4kr6JYtGgRt956a/HX3bp148MPPyQiIqLcs3z++edMmjSJJk2aMHLkSGrWrMnZs2f5+OOPefTRRxk/fjyDBg0q91yXLF++vMTXzZo148MPP9TDI0XsoHIj4oLq1KlDnTp1ir8ODQ3F09OT1q1bOy9UGQgNDSU0NLTcjxsfH8+LL75Ir169mDZtGmbz/5/EfuCBB5g6dSqvvfYasbGxdOrUqdzzXYm/v3+l+/0XKSualhKpJNauXcs999xDixYteOCBB9i0aVOJ15OSkhgzZgy33norrVq1YsCAARw4cKDEmKysLKZPn86dd95JixYtuO+++/j4449LjOnRowfTpk1jwIABtG3blsmTJwOQnp7O5MmT6dSpEy1atOAvf/kLW7duLfG+06dP8+mnnxZPRV1pWmrz5s08/vjjtGnThjvuuIPJkyeTkZFR/Pr27dt54oknaN++Pc2bN6dHjx688cYb2Gw2u79XS5YswcfHh8mTJ5coNpeMHz+e6tWr8+abbxZv69+/P/379y8x7vdTRfbkuzRt9tVXX/Hss8/Spk0b2rdvz6RJk8jJySk+1rZt29i2bVvx/q90rN/79ttv6dOnDy1atOD222/nH//4B7m5ucWvFxQUMGXKFLp06ULz5s35wx/+wDvvvGP3902kolC5EakEzpw5w5IlS3juueeYP38+hmHwzDPPkJqaClx8CvgjjzzC/v37+fvf/86sWbOw2Ww8/vjjHD16FID8/Hwee+wx1q9fz+DBg1m4cCG33HILkyZNYvHixSWO9/777xMbG8sbb7zBgw8+SEFBAQMGDGDDhg2MHj2aBQsWEBUVxZNPPllccBYsWEB4eDhdu3a96lTUDz/8wJNPPklwcDBz5sxh/PjxfPfddzz77LMAHDp0iIEDBxa/vmjRItq2bcuCBQv417/+Zff367vvvuP222/H19f3iq97enpy5513snPnTi5cuGD3fh3J99JLL1GzZk0WLlzIk08+ySeffFL8fX7ppZdo2rQpTZs25cMPP6RZs2bXPfbnn3/O008/TXR0NG+++SYjR45k/fr1jBgxovjC5KlTp/LDDz8wceJEli1bRs+ePXnttdfsuj5JpCLRtJRIJWCz2XjzzTdp0KABAF5eXgwaNIhdu3bRs2dPVqxYQXp6OqtXr6ZmzZoAdOnShT/+8Y/MmzeP+fPns3btWuLj41m1ahW33HILAJ07d6aoqIiFCxfyyCOPEBwcDEBERATPP/988VmPNWvWcOjQIdasWUOrVq2K99+/f39mzpzJJ598QtOmTfH09CQ0NPSq0yvz58+ncePGJc6YeHt7M3v2bM6dO8ehQ4fo1KkTM2bMKD727bffzsaNG9m+fTv333//db9X6enp5OTkFH8frqZu3boYhkFSUhIhISHX3S/gUL6uXbsWXzDcsWNHNm/ezMaNGxk7diwNGzbE398fwK6pKMMwmDlzJp07d2bmzJnF2+vVq8fAgQP54Ycf6NatG9u2baNTp0706tULgA4dOuDr62v3/59IRaFyI1IJhISEFBcbgNq1awMXp5kAtm7dSpMmTYiMjKSoqAgAs9lMly5dWL9+PQDbtm2jZs2axcXmkgceeICPP/6Y3bt307VrVwAaNGhQYjpn69athIeH06xZs+L9A3Tv3p3XX3+djIwMgoKCrvn/kJ+fz/79+3nmmWdKbL/nnnu45557AOjduze9e/emoKCAxMRETpw4wf79+7FarRQWFtr1vbp0FsNkMl1z3KXXHZnuciTf70tLVFQUp0+ftvtYv5WQkMDZs2cZOnRoie9/+/bt8ff3Z/PmzXTr1o0OHTrwwQcfcO7cObp3707Xrl15+umnb+iYIq5M5UakEvj99MrvfzCnp6dz4sSJq05v5OXlkZGRQbVq1S577dK2zMzMy7Zdkp6eTkpKylX3n5KSct1yk5GRgWEYhIWFXXVMfn4+r7zyCp999hlFRUXUqlWLNm3a4O7ubveaMCEhIfj5+XHy5Mlrjrt0HVCNGjXs2q+j+Xx8fEp8bTabb3hdm/T0dACmTJnClClTLns9OTkZgEmTJhEVFcX69euLx7Vp04bJkyfTtGnTGzq2iCtSuRGpAgICArj11luZMGHCFV/39PQkKCiIEydOXPZaSkoKwDWnLgICAqhXr16JKZHfqlWr1nUz+vv7YzKZSEtLK7HdYrGwdetWWrZsyezZs/n666+ZO3cunTp1Ki51HTt2vO7+f6t79+5s2rSJnJwc/Pz8io9z7NgxYmNjsVqtfPvttzRr1qxE2bJarSX289uLdeHiNS2lkc9RgYGBAEyYMKHErfaXXCqWnp6eDB8+nOHDh5OUlMT333/PwoULGTt2LF999VWZZhQpT7qgWKQKuPXWWzl27Bj169enRYsWxf+tX7+ejz76CDc3N9q3b8/p06fZuXNnifeuX78eDw8PWrZsec39nzlzhrCwsBL737p1K0uXLsXNzQ3gincmXeLn50eTJk3YsGFDie0//fQTTz31FGfPnmXnzp106NCBO++8s7g47Nu3j7S0NIemj4YOHUp+fj5Tpkwpft++ffvo3bs3Q4cOZfr06SQmJpaYsvH39+fs2bMl9vPLL7+U+Lq08sG1v1e/Fx0dTVhYGKdOnSrx/Y+KimLWrFkcOHCA/Px87rnnnuK7o2rUqMHjjz9Or169Lvv/EqnodOZGpAoYOHAgn332GQMHDmTw4MGEhITw5ZdfsmbNGl544QUA+vTpw6pVqxg5ciTPPvsstWvX5rvvvuOTTz5h5MiRxWcHrqRPnz689957DBo0iGHDhlG9enW2bNnC22+/Tb9+/fDw8AAunmE4cOAA27Ztu2JZevbZZxk+fDijRo2iT58+pKWlMWvWLLp3706TJk1o2bIlX331FatXr6ZBgwYcOnSIRYsWYTKZyMvLs/v7ERMTw6uvvsoLL7xAYmIijzzyCLVq1WLUqFHMmzcPq9VKx44dS6z83L17d7777jumTp1afCfVunXrSuy3tPJd+l79+uuvbN269bpTRm5ubowePZrJkyfj5uZG9+7dyczMZOHChZw7d45mzZrh7e1Ns2bNWLBgAR4eHsTGxnLs2DE+/fTT4muaRCoLlRuRKiAyMpIPPviAWbNm8fLLL1NQUEC9evWYOnUqf/rTn4CL14CsXLmSWbNmMX/+fLKzs4mOji4x5mp8fX15//33mTVrFjNmzCArK4uaNWsyduxYBg8eXDxu8ODBTJs2jSeeeIJ33333sv10796dt956izfeeIOnn36akJAQ7r33Xp577jng4qMmCgsLmTt3LhaLhVq1ajF8+HCOHDnCd999d9m00bX06tWLmJgYli9fzvz580lJSSE4OLh4jZ+lS5fSt29f/vGPf9C0aVP69u1LYmIin376KR9++CG33nor8+bN49FHHy3eZ2nme/zxx9m3bx9Dhgxh+vTp113F+c9//jN+fn4sXbqUDz/8EF9fX9q2bcvMmTOLLzD/n//5H+bOncs777xDSkoKYWFh/OlPfyr+/opUFiZDT2YTEblMWloaK1eupG/fvnZdMyQirkPlRkRERCoVXVAsIiIilYrKjYiIiFQqKjciIiJSqajciIiISKWiciMiIiKVisqNiIiIVCoqNyIiIlKpqNyIiIhIpaJyIyIiIpWKyo2IiIhUKv8LiAJUP+CEUGoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Test for normality\n",
+    "normality_test(model1_ols);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results of the normality test indicate significant deviations from normality due to the large Jarque-Bera statistic and extremely low p-value. Also, the positive skewness and extremely high kurtosis further confirm the non-normality of the residuals."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Test for Homoscedasticity**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assess whether the variance of the residuals (errors) is constant across observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def homoscedasticity_test(ols_model):\n",
+    "    \"\"\"\n",
+    "    tests for homoscedasticity by taking in an OLS model, and reporting out different test features\n",
+    "    and plots residual vs fitted plot\n",
+    "    \"\"\"\n",
+    "    predicted_y = ols_model.predict()\n",
+    "    resids = ols_model.resid\n",
+    "\n",
+    "    fig, ax = plt.subplots()\n",
+    "\n",
+    "    sns.regplot(x=predicted_y, y=resids, lowess=True, ax=ax, line_kws={'color': 'red'})\n",
+    "    ax.set_title('Residuals vs Fitted', fontsize=16)\n",
+    "    ax.set(xlabel='Fitted Values', ylabel='Residuals')\n",
+    "\n",
+    "    bp_test = pd.DataFrame(sms.het_breuschpagan(resids, ols_model.model.exog), \n",
+    "                           columns=['value'],\n",
+    "                           index=['Lagrange multiplier statistic', 'p-value', 'f-value', 'f p-value'])\n",
+    "\n",
+    "    gq_test = pd.DataFrame(sms.het_goldfeldquandt(resids, ols_model.model.exog)[:-1],\n",
+    "                           columns=['value'],\n",
+    "                           index=['F statistic', 'p-value'])\n",
+    "\n",
+    "    print('\\n Breusch-Pagan test ----')\n",
+    "    print(bp_test)\n",
+    "    print('\\n Goldfeld-Quandt test ----')\n",
+    "    print(gq_test)\n",
+    "    print('\\n Residuals plot ----')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Breusch-Pagan test ----\n",
+      "                                     value\n",
+      "Lagrange multiplier statistic  2024.105446\n",
+      "p-value                           0.000000\n",
+      "f-value                         114.820200\n",
+      "f p-value                         0.000000\n",
+      "\n",
+      " Goldfeld-Quandt test ----\n",
+      "               value\n",
+      "F statistic  0.79985\n",
+      "p-value      1.00000\n",
+      "\n",
+      " Residuals plot ----\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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B/v73v/PQQw/x4YcfsmjRIsA9266rq+PGG2/MBAngJuw+8cQTAJnbdmXhwoUcdNBBbN68mRtuuCFzELRtm1/+8pddNq5M7wJ78MEHc2ZdXn/99S6LvaV3SD377LM5l69fvz7z+QI3L6c7s2fPZtu2bfz5z39m06ZNOdc9/PDDmd+lr9JLUD/96U9xHIfPfvazOdeHQiFs22b9+vXceeedOdft2LGD1atXo6pqzo6roebz+QBy/p07Xtfa2pq57OMf/zgHHHAA77//PjfccENOwvbWrVv5yU9+QnV1dWbJyuPxcPbZZ2NZFldeeWXO8zzzzDM8+OCDQ/BbCTE49ullqZ78/ve/Z8WKFZx11lmAu5X2hz/8Ieeffz47duzIfNCvuuoqvv71r/Ptb3+bp556iksuuYQ77rijy90tQnTnlFNO4f777+eVV17h2muv5ZFHHsHr9bJgwQKuvvpqfvKTn3DBBRdwwAEHMGXKFDZv3kx1dTWapvGLX/yCcePGAXDWWWfx2GOPsXr1apYvX87ixYvxer2Zs+85c+ZkllG7c/3113Puuedy55138q9//Yv99tuPjz76iC1btrBkyRLWrFmTc/uTTz6Z3/72t2zbto0TTjiBgw46iPr6et5++21OO+00/vnPf+bc/oILLuCtt97illtuYdWqVUyZMoU9e/awdu1afD4fU6ZMYceOHT1uOd9vv/0477zzuOuuuzjllFNYtmwZpaWlbN26lXXr1hEMBnstApjt4IMPZvLkydTU1ODz+TjppJM63eaHP/wh55xzDtdffz333Xcfs2fPJhKJ8NZbb5FIJPjqV7/abS7TUJgyZQqaprF+/XrOP/985s2bx9VXXw3A9OnTWb9+Peeddx4zZ87kZz/7GcFgkBtvvJHzzz+fO+64g8cee4z58+cTj8d58803MQyDE044gXPOOSfzHJdccgmrV6/mtdde47jjjuPggw+mvr6e1atXs3Tp0i5n5oTIB/v0zE1PPvjgA+677z6WLl2a+Ul3a964cWOm9sWFF17Iaaedxv7778+3vvUtjjzySO64446RHLoYpa655hq8Xi9btmzhtttuy1x+zjnn8Ne//pXjjz+e3bt38/zzzxONRvnkJz/JypUrcw7EXq+X22+/nYsuuojy8nJee+01/vOf/1BQUMAll1zCvffeS1FRUY/jmDZtGv/4xz84++yzicfjPP/88wSDQX7zm99w1FFHdbp9QUEBf//73zn99NPRNI0XXniBtrY2rr32Wn7wgx90uv0JJ5zA7bffzsEHH0xNTQ3/+c9/iEQimUAofXB9/vnnexzn9773Pa699lrmz5/Pe++9x6pVqwiHw6xYsYKHH36YAw44oMf7Z8vOsTnmmGO6fI0WL17MPffcw4knnkg4HGbVqlW8//77HHjggdx88819ymMZTOXl5Vx33XVMmTKFt956K+f1uu6665g/fz5btmzhtddeY/v27QDMnDmTBx98kAsvvJBgMMhLL73EunXrWLBgAddffz033XRTTqKw1+vlj3/8I1dccQWlpaW88MIL1NbWcsUVV3DFFVcM6+8rRH8oTk/7Lfch3/3ud6mpqclskVy0aBFf+tKXutyCW1FRwbp16zjrrLP461//ykEHHZS57oYbbuBf//oXjz/++LCNXQghhBDtZOamG3PnzmXTpk1Mnz4987Nnzx5uuOEG2traOOCAAygoKGDt2rU591u/fj3Tpk0boVELIYQQQnJuuvGVr3yFb33rW9xyyy18+tOfZvfu3fzgBz9g0qRJmS2VX/7yl7n11lsZP348ixYt4rHHHuOll17qlHAohBBCiOEjwU03TjrpJG666Sb+8Ic/8Ic//IHi4mKOOeaYnN0cl1xyCYFAgJtuuok9e/Ywe/ZsbrnlFj7+8Y+P4MiFEEKIfZvk3AghhBBiTJGcGyGEEEKMKRLcCCGEEGJM2edybg466CCSyWRO/x4hhBBC5Le6ujq8Xi9vvvlmr7fd54KbRCLRZYM/IYQQQuQv0zTpa5rwPhfcVFZWAvDcc8+N8EiEEEII0VfHHntsn28rOTdCCCGEGFMkuBFCCCHEmCLBjRBCCCHGFAluhBBCCDGmSHAjhBBCiDFFghshhBBCjCkS3AghhBBiTJHgRgghhBBjigQ3QgghhBhT9rkKxUIIIcRgsm2HTTUthNuShAq8zJpcjKoqIz2sfZoEN0IIIcQAra2uY+WqampqI5iWja6pTK4sZMXyuSyeKw2aR4osSwkhhBADsLa6jltXrmXLzjB+n0ZpkQ+/T2PLrjC3rlzL2uq6kR7iPkuCGyGEEKKfbNth5apqYnGT8mIfPo+Gqir4PBrlIR+xhMnKVdXYdt+6WIvBJcGNEEII0U+balqoqY1QVOBBUXLzaxRFoSjgoaY2wqaalhEa4b5NghshhBCin8JtSUzLxqN1fRj16CqmZRNuSw7zyARIcCOEEEL0W6jAi66pGJbd5fWG6SYXhwq8wzwyARLcCCGEEP02a3IxkysLaY0aOE5uXo3jOLTGDCZXFjJrcvEIjXDfJsGNEEII0U+qqrBi+VwCPp2GcIJE0sK2HRJJi4ZwgqBPZ8XyuVLvZoRIcCOEEEIMwOK5FVy6YjEzJoaIJy2aWhPEkxYzJoa4ZMViqXMzgqSInxBCCDFAi+dWsHD2OKlQnGckuBFCCCH2gqoqzJlaMtLDEFlkWUoIIYQQY4oEN0IIIYQYUyS4EUIIIcSYIsGNEEIIIcYUCW6EEEIIMaZIcCOEEEKIMUWCGyGEEEKMKRLcCCGEEGJMkeBGCCGEEGOKBDdCCCGEGFMkuBFCCCHEmCLBjRBCCCHGFAluhBBCCDGmSHAjhBBCiDFFghshhBBCjCkS3AghhBBiTJHgRgghhBBjigQ3QgghhBhTJLgRQgghxJgiwY0QQgghxhQJboQQQggxpkhwI4QQQogxRYIbIYQQQowpEtwIIYQQYkwZdcHN5s2bWbp0KQ888MBID0UIIYQQeWhUBTeGYfDtb3+baDQ60kMRQgghRJ7SR3oA/XHLLbdQUFAw0sMQQgghMmzbYVNNC+G2JKECL7MmF6OqykgPa582aoKbN954g3vvvZcHH3yQo48+eqSHI4QQQrC2uo6Vq6qpqY1gWja6pjK5spAVy+eyeG7FSA9vnzUqlqXC4TBXXnklP/jBD5g4ceJID0cIIYRgbXUdt65cy5adYfw+jdIiH36fxpZdYW5duZa11XUjPcR91qgIbq699lqWLFnCKaecMtJDEUIIIbBth5WrqonFTcqLffg8Gqqq4PNolId8xBImK1dVY9vOSA91n5T3y1IPPvggb775Jo888shID0UIIYQAYFNNCzW1EYoKPChKbn6NoigUBTzU1EbYVNPCnKklIzPIfVjez9zcf//9NDQ0cPTRR7N06VKWLl0KwDXXXMOnPvWpER6dEEKIfVG4LYlp2Xi0rg+jHl3FtGzCbclhHpmAUTBz88tf/pJ4PJ5z2QknnMBll13GySefPEKjEkIIsS8LFXjRNRXDsvGpWqfrDdNNLg4VeEdgdCLvg5vx48d3eXl5eTmTJ08e5tEIIYQQMGtyMZMrC9myK4w3pOYsTTmOQ2vMYMbEELMmF4/gKPddeb8sJYQQQuQbVVVYsXwuAZ9OQzhBImlh2w6JpEVDOEHQp7Ni+VypdzNC8n7mpisfffTRSA9BCCHEPm7x3AouXbE4U+cmEjPQNZUZE0NS52aEjcrgRgghhMgHi+dWsHD2OKlQnGckuBFCCCH2gqoqst07z0jOjRBCCCHGFAluhBBCCDGmSHAjhBBCiDFFghshhBBCjCkS3AghhBBiTJHgRgghhBBjigQ3QgghhBhTJLgRQgghxJgiwY0QQgghxhQJboQQQggxpkhwI4QQQogxRYIbIYQQQowpEtwIIYQQYkyR4EYIIYQQY4oEN0IIIYQYU/SRHoAQQuQT23bYVNNCuC1JqMDLrMnFqKoy0sMSQvSDBDdCCJGytrqOlauqqamNYFo2uqYyubKQFcvnsnhuxUgPTwjRR7IsJYQQuIHNrSvXsmVnGL9Po7TIh9+nsWVXmFtXrmVtdd1ID1EI0UcS3Agh9nm27bByVTWxuEl5sQ+fR0NVFXwejfKQj1jCZOWqamzbGemhCiH6QIIbIcQ+b1NNCzW1EYoKPChKbn6NoigUBTzU1EbYVNMyQiMUQvSHBDdCiH1euC2Jadl4tK6/Ej26imnZhNuSwzwyIcRASHAjhNjnhQq86JqKYdldXm+YbnJxqMA7zCMTQgyEBDdCiH3erMnFTK4spDVq4Di5eTWO49AaM5hcWcisycUjNEIhRH9IcCOE2OepqsKK5XMJ+HQawgkSSQvbdkgkLRrCCYI+nRXL50q9GyFGCQluhBACWDy3gktXLGbGxBDxpEVTa4J40mLGxBCXrFgsdW6EGEWkiJ8QQqQsnlvBwtnjpEKxEKOcBDdCCJFFVRXmTC0Z6WEIIfaCLEsJIYQQYkyR4EYIIYQQY4oEN0IIIYQYUyS4EUIIIcSYIsGNEEIIIcYUCW6EEEIIMaZIcCOEEEKIMUWCGyGEEEKMKRLcCCGEEGJMkQrFQgghumTbjrSiEKOSBDdCCCE6WVtdx8pV1dTURjAtG11TmVxZyIrlc6WJqMh7siwlhBAix9rqOm5duZYtO8P4fRqlRT78Po0tu8LcunIta6vrRnqIQvRIghshhBAZtu2wclU1sbhJebEPn0dDVRV8Ho3ykI9YwmTlqmps2xnpoQrRLQluhBBCZGyqaaGmNkJRgQdFyc2vURSFooCHmtoIm2paRmiEQvROghshhBAZ4bYkpmXj0bo+PHh0FdOyCbclh3lkQvSdBDdCCCEyQgVedE3FsOwurzdMN7k4VOAd5pEJ0XcS3AghhMiYNbmYyZWFtEYNHCc3r8ZxHFpjBpMrC5k1uXiERihE7yS4EUIIkaGqCiuWzyXg02kIJ0gkLWzbIZG0aAgnCPp0ViyfK/VuRF4bFcFNQ0MD3/nOdzjkkENYunQpF110ERs2bBjpYQkhxJi0eG4Fl65YzIyJIeJJi6bWBPGkxYyJIS5ZsVjq3Ii8NyqK+H3ta19DVVVuu+02gsEgv/71r7ngggt45plnCAQCIz08IYQYcxbPrWDh7HFSoViMSnkf3DQ1NTFlyhS+9rWvMXfuXAAuueQSPvOZz1BdXc2iRYtGeIRCCDE2qarCnKklIz0MIfot74Ob0tJSbrzxxsx/19fXc/vttzNhwgTmzJkzgiMTQgghRD7K++Am2//8z/9w33334fV6+d3vfkcwGBzpIQkhhBAiz4yKhOK0888/n/vvv59TTz2VSy+9lPfff3+khySEEEKIPDOqgps5c+awYMECfvzjHzNlyhT+8pe/jPSQhBBCCJFn8j64aWho4NFHH8WyrMxlqqoye/ZsamtrR3BkQgghhMhHeR/c1NbWcsUVV/D6669nLjMMgw8++IDZs2eP4MiEEEIIkY/yPrjZb7/9OPzww/nhD3/Im2++yfr167nqqqsIh8NccMEFIz08IYQQQuSZvA9uFEXhV7/6FYcccgjf+ta3OOOMM2hpaeGvf/0rkyZNGunhCSGEECLPjIqt4EVFRVx77bVce+21Iz0UIYQQQuS5vJ+5EUIIIYToDwluhBBCCDGmSHAjhBBCiDFFghshhBBCjCkS3AghhBBiTJHgRgghhBBjigQ3QgghhBhTJLgRQgghxJgiwY0QQgghxhQJboQQQggxpkhwI4QQQogxRYIbIYQQQowpEtwIIYQQYkyR4EYIIYQQY4oEN0IIIYQYUyS4EUIIIcSYIsGNEEIIIcYUCW6EEEIIMaboIz0AIYQQYjDYtsOmmhbCbUlCBV5mTS5GVZWRHpYYARLcCCGEGPXWVtexclU1NbURTMtG11QmVxayYvlcFs+tGOnhiWEmy1JCCCFGtbXVddy6ci1bdobx+zRKi3z4fRpbdoW5deVa1lbXjfQQxTCT4EYIIcSoZdsOK1dVE4ublBf78Hk0VFXB59EoD/mIJUxWrqrGtp2RHqoYRhLcCCGEGLU21bRQUxuhqMCDouTm1yiKQlHAQ01thE01LSM0QjESJLgRQggxaoXbkpiWjUfr+nDm0VVMyybclhzmkYmRJMGNEEKIUStU4EXXVAzL7vJ6w3STi0MF3mEemRhJEtwIITqxbYcN25tZva6WDdubJV9B5K1Zk4uZXFlIa9TAcXLfp47j0BozmFxZyKzJxSM0QjESZCu4ECKHbKkVo4mqKqxYPpdbV66lIZygKODBo6sYpk1rzCDo01mxfK7Uu9nHyMyNECJDttSK0ca2HQr8Hk74+DQqSwPEEiZNrQniSYsZE0NcsmKxBOX7IJm5EUIAnbfUpnee+FQNb0ilIZxg5apqFs4el/dnwVKpdt/QcZZR01TKigMcumAiS6oq5N99HybBjRAC6N+W2jlTS0ZmkH0wXMtqEkCNrPQsYyxuUlTgwaN5MCyb2qYoT7++lbnTSuTfYx8mwY0QAsjeUuvp8nqPrhKJGXm9pba7A156We3SQVqikLykkTWWZhnF0JCcGyEEMPq31A5XpVrJSxp5UrhP9EaCGyEEMPq31A7HAU9K/ecHKdwneiPBjRACaN9SG/DpNIQTJJIWtu2QSFo0hBN5v6V2OA54MmOQH0b7LKMYegMObiKRCHv27AEgmUzyxz/+kZ/85Ce88cYbgzY4IcTwWjy3gktXLGbGxBDxpDWqttQOxwFPZgzyw2ifZRRDb0AJxe+88w5f/vKXOfPMM/n2t7/NT37yE+677z5CoRD33HMPt9xyC8cee+xgj1UIMQwWz61g4exxo24nUPqAt2VXGG9IzZlZSR/wZkwM7dUBLzuA8qlap+tlxmB4SOE+0ZsBzdzcdNNNzJo1i89//vPE43EeeeQRzj77bF5//XVWrFjB73//+8EepxBiGKmqwpypJSzbr5I5U0fHltrhWFaTGYP8MZpnGcXQG9DMzdq1a7npppuYOnUqzz//PPF4nM985jMAnHzyyTz88MODOkghhOiL9AEvvU07EjPQNZUZE0ODsk1bZgzyy2idZRRDb0DBjaqqeL3utOsLL7xAKBRi0aJFgJuL4/f7B2+EQgjRD0N9wBvqAEr0T3qWUYhsAwpuFixYwMqVK/H7/TzxxBMcffTRKIpCQ0MDt912GwsWLBjscQohRI+Gs2KwzBgIkd8GFNxceeWVfPnLX+axxx6jrKyMr33tawB8+tOfxrZtbr/99kEdpBBC9GQkKgbLjIEQ+WtAwc0BBxzA008/zcaNG5k7dy7BYBCAa6+9lmXLllFRIdOyQuS7sdIbabhaLgghRo8B95YqLCxk8eLFOZedeOKJez0gIcTQGyu9kaTHkBCiK30ObpYvX96pImd3FEXh2WefHfCghBBDZyzNdIyVTuZCiMHV5+DmYx/7WJ+DGyFEfhprMx1joZO5EGLw9Tm4+dnPfjaU4xBCDDHbdnhh9Q4217Tg92kwBmY6pGKwEKIrA865Aaivr8cw2it12rZNLBbjzTff5KyzzhqUAQI0Nzdz44038q9//YtIJMK8efO44oorOOiggwbtOYQYy9I5NptrWghHk0RiEG4zKA35CPravwZG20zHcLRcEEKMPgMKbtatW8fll1/O5s2bu7xeUZRBDW4uv/xyGhoauPHGGykrK+Oee+7hwgsv5IEHHmD27NmD9jxCjEXZOTZ+n0YkrqAAScOirilGRWkgE+CMtpkOVVU4/Zg53Hzv2+xujFHo1wn6dUzLkYrBQuzDBtRb6oYbbiAcDnPVVVfxsY99jMMPP5z/+Z//4aijjkJRFO66665BG+DWrVt56aWXuOaaazjooIOYNWsW3//+9xk/fjyPPvrooD2PEGNRxxybwoAHr67iOKCp7vVN4QQOo7M30trqOh54fgOGaZNImtQ1x9leGyHclpQeQ0LswwYU3Kxdu5ZvfvObXHDBBXzqU58iGo1y9tln8/vf/57jjjuOu+++e9AGWFpayv/93//lVD1WFAXHcWhpaRm05xFiLOq4m0hRFEqL/KiKgmW7aTdJw6Q1mhy05pLDJT0jtWVnmFCBl6nji6go9ePz6ng8Gp87Zo4ENkLsowYU3CSTSWbOnAnArFmz+OijjzLXnX766axZs2ZQBgcQCoU46qijMr2sAJ544gm2bdvG4YcfPmjPI8RY1L6bqP2jHvTrVJQG8HpUHMB2IJEYXd2UO85I+TwamqpQFPQyoSyAadnc//wGbNvp/cGEEGPOgHJuJk2axPbt2znooIOYPn06kUiEHTt2MGXKFLxe75DOqLz11ltcffXVHHvssSxfvnzInkeIfDLQasLd7SYK+nUCvgIiUYN40uLCUxdw1LIpo2LGBqS+jRCiZwMKbk444QR++ctfEggEOOmkk5g1axY33XQTF110EX/605+YOnXqYI8TgGeffZZvf/vbLF68mBtvvHFInkOIfLM31YR72k0EkDBtZk4uHlWBDUh9GyFEzwa0LPX1r3+dAw88kPvvvx+A733vezz77LN89rOf5dVXX+Ub3/jGoA4S4C9/+Qvf+MY3OPLII7ntttvw+/2D/hxC5JvsvBK/T6O0yIffp2WqCa+truvx/qqqsGL5XAI+nYZwgkTSwrYdEklr1OXYZMuekerKaNv1JYQYXAOaufH5fNx8880YhgHAEUccwaOPPsp7773H/PnzmTZt2qAO8p577uHHP/4x5557LldffTWqOqCYTIhRZbCqCS+eW8GlKxZnZn8iMQNdU5kxMTTqekmlSX0bIURP9qqIn8fTPiU8derUIVmO2rx5Mz/96U85/vjjufjii2loaMhc5/f7KSoqGvTnFCIfDGZeyeK5FSycPW5MdAGH9hmpW1eupSGcoCjgwaOrGKYt9W2EEAMLbs4777xebzNYtW6eeuopDMPgmWee4Zlnnsm57rTTTpO2EGLMGuy8ElVVxlRy7VickRJCDI4BBTfpdgvZotEoGzduJBgMcsIJJ+z1wNK++tWv8tWvfnXQHk+I0WKk+yYNdIfWcI5juGek8uU1EUL0bEDBTXdF+lpaWrj44ouZNWvWXg1KCDGyeSV7s0NruMcxXDNSg/2aSKAkxNAZ1Mzc4uJivvKVr3DnnXcO5sMKsU8aqZ1Oe7tDa6yNYyjGsra6jmtue4Wf3vk6v/r7an565+tcc9srw/o7CTGWDfq2I8dxcpJ+hRADl84rmTExRDxp0dSaIJ4cumrCXVX+VVUFn0ejPOQjljBZuap6yCv/5ss4hmIs+RS0CTFWDWhZ6o033uh0mWVZ7N69m9/85jfMnz9/rwcmhHANZ15JvlT+zZdxDPZYBmt7vxCiZwMKbs4999xOH3JwZ20mTpzI1VdfvdcDE0K0G668knyp/Jsv4xjsseRT0CbEWDag4Karbd6KolBYWMi8efOkyJ4Qo9RI79DKt3EM9ljyKWgTYiwbUHDzsY99bLDHIYQYRt3t1MmXyr/5Mo7BHks+BW1CjGV9Dm4efPDBfj3wZz/72X4ORQgxHHrb0pyp/NuSwOtVURUF23FIJm2C/uGp/JtPFYgHcyz5FLQJMZYpTlcV+bqw33775d4x9aHMvnv2B/XDDz8cjPENumOPPRaA5557boRHIsTwS+/UicVNigo8eFKzCK1Rg4BP59LUDqwH/rWBlc+tpy1m4uCgoFAQ0FlxbBWnHz1nWMebD/V2BnMsmX+DhNlloDQUu+CEGAv6c/zu88xN9oN9+OGHXHnllXzta1/jk5/8JJWVlTQ1NbFq1SpuueUWrr/++gEMWwgxlPq6U8d2HJ58ZQuqolBR6kdBwcEhnrR48pUtzJ5czOK5FcNShC6femINdCwdX6eFs8dJ2wghhlifg5vJkydn/v6Nb3yDr33ta3zlK1/JXDZ+/HjOOussDMPgF7/4BUcdddTgjlQIsVf6slNnR22EvzzxIbG4ybgSf87tCgNOTgD0wPMbhmVGJZ96YvV3LD3N9vzwK4fmRdAmxFg0oG1NGzduZP/99+/yupkzZ7Jjx469GpQQYvC179Tp+mPv0VUSSYs9DdEeA6DNO1u4+d63pQhdL3or1vfuxnrmTC1h2X6VzJlaIoGNEINoQMHNjBkzeOihh7q87t5776WqqmqvBiWEGHzZO3W6Ypg2qgK243QbAOm6SlvMJBa3hr1ysG07bNjezOp1tWzY3jws1YkHKp8qLAuxLxrQVvBLL72Ub37zm2zZsoVjjz2WsrIy6uvrefrpp9mwYQO33XbbYI9TCLGXZk0uZnJFIRtrmikMeNA0FZ9XQ6F9p8748gIaW2Kdtio7jkPCsGmNJrEsm4KQd1iL0A1VYvFQ5Q1JsT4hRtaAgpsTTjiBW2+9lVtvvZVf//rXOI6DqqosXbqUO++8k4MOOmiwxymE2EvvbqynNZakLW4SiRqoKnh0jaICL4ZpE/TpnPvJ/bj/+Q05W5WjcZOm1jiGaWNZDg4QbjPQNY2gP/crZCiK0HXe4eXBsOzM8s6lA9xdNJQ7saRYnxAja0DBDcDy5ctZvnw5iUSClpYWSkpK8Hql8JQQ+Sg7QCgv9hFpM0iaNgnDItkSZ+akEP/16fksnluBoiiZmi4eTaGpNZFZPlFVcBxIGhZ1TTEqSgM5AU66CF1h0MOG7c17PSMyVL2YhipgSpNifUKMrD4HNzt37qSiogKPx8POnTs7XV9fX5/z35MmTdr70QkhutSf5RTTtLnr8Q8JtyUoKfTh9+mEgl4Sho1lufVVCgMeFs4eB7R3Iv/Hc+v5YHMjluWgquD16JQW+WhqTZBImtiOTVNrnICvAEVRMktb5cV+7n78Q2rq9n5GpE87vPa08sLqHRQX+voUSA1H80op1ifEyOpzcHPsscdy7733smjRIpYvX95l48xs+VrET4jRrj/LKWur67jr8Q/YsL0FcEgkLTy6RmnIR9CnAxq6prKzri0n/2Px3AoCPp0f/+k1PKncnHR+DgrUNbmBUdKwiScsVFWhNWagAnXNMXbXRykM6pQEvJi2M+AZkd6Wd0zLpjmS4PaH30NVlT4FUsORD5NPFZaF2Bf1Obj56U9/ytSpUzN/7y24EWJfMxxF7fqznJK+bTiSBAU0VQGU3CUln95t/kckaqAARUFPzu8R9OlUlAZobImTNCxa2pIEfDplIR+76tuIJy1URSFhmIR1ldIiP+Uh34BmREIFXjRNpS1uoKmqu+MoFWRF4yZ1zTEs28Hv0yjw921pabjyYdIzYL0V6xuO940Q+5o+BzennXZa5u+nn376kAxGiNFqONoE9Gc5BcjctrTIS6LRBBRURUHVwLQcmsIJAhV6t/kfPeWNBH06akmASMzg88dWoWkK9z27nnjSQlPd53GApGFnAqmBzIi0xpIkkiaRmBtoqaqCR9coKfLS3JrAshx8Xo3CoBelm9eiY6AwnPkwvVU1zqf2EkKMJQOqcwPwxhtvsHr1agB27NjBRRddxCmnnMKtt946aIMTYjTorVjbYBW1689ySvZtfV4Nj65i206qF5yCqioYpkU8YdIaM5hcWdgp/yOdN9IaNejYgs5xHCKpvJGTDp3By+/uIpY0MwGIorgBjqa6TTebWuPomoJp2X2eEVlbXcfv7n8H23ZnndK/ciJpUtsYdQMpTaGs2E/2q9Hxteiot9+ru9djoNJVjTsW6xuu940Q+6IBBTcPPfQQ5513Hs8++ywA1157LW+88QbTp0/n97//Pf/3f/83qIMUIl8NZ7G2vlQYTgcP2bdVFIXSIj+qomDZDrbjAA627dAcSXab/5HOGwn4dBrCCRJJC9t283YawonM/bbsClNTG6Ew4C5fZccLipIOpGyicTNnRqSnonzZr+uE8gCVpUG8Hi3zmJYNODCuJJDKHer+teior7/XUC4NSZE/IYbWgLaC33HHHZx22mlceeWVNDQ08PLLL3PFFVdw4YUX8qc//Yl7772Xiy66aLDHKkTeGc5ibd0tp6QL7MUTJo4DhUEPqqLk3Dbod/Nk0vVq3GOmwuSKQs47ef9ul0D6kjeyel0tpmVTEvQRbjNIGhbu8NzXQwFsByJxk7lTS5g1ubjX5ZiOr2vQrxPwFbg7vGybRMKiOZLoNPOS1tvSUl/zYYaKFPkTYmgNKLjZtGkT3/ve9wB48cUXcRwn04p84cKF/OpXvxq0AQqRz4YqObWrJNOuthfnFNizHXRN5e7HP+S0o+dQGvJTUxehpNCL36e3BwhJi6ZIkskVBfz80sPR9Z4ncHvLG0kHXaZlUxryUdcUw0xtH1dS7RxsxyHg01ixfC7vbqzvNSnaspxOr6uiKPi9GqDh92iEo0kiMZPCgGdAW61HsuO4FPkTYmgNKLgJhUK0tbUB8MILLzBp0iRmzJgBwLZt2ygtLR20AQqRz4YiObWnWY3s7cUdC+xpqkJJkY8NO5q57k53C3csYRKNG3h1jdJiHx5VJRI3KS7wct7JB/Qa2KT11A07O+gqD/ncGaJwAsO0MhWNCwMeLvv8UhbOHsc1t72SkxTtOA6OAwGvRms0yT+eW++OrZvX1XEconETn0fDo6k0tCQoCg5sq/VIdRyXIn9CDK0B5dwccsgh/OY3v+EPf/gDzzzzDCeffDIATz31FL/+9a857LDDBnWQQuSrwU5O7S3JFODSFYuZPqGI5kgSy3JQFPB5dSrLgnh0lXjSxDBsTNuhsjSA16ORNC1qG2KEo0lmTAxxyV5W4M3WMYdFUxQmlAcpK/YT8HsoLw5w5bkHsbSqstNyTDRusrO+jd0NbdQ2xYjFTT7Y3Ej19iYmVxYSbksST5i0xQ3iSYu2mMHO+jZqm2MkDAvbcbBsm3A0SVNrgnjSGvTfbygMd1KzEPuaAQU33//+9yktLeXWW2/lE5/4BBdffDEA119/PZMmTeKKK64Y1EEKka8GMzm1r0mmC2eP47yTD6Ag4GFcSYCJ4wqZVFFAwKfTFE7gOKDrCpZlo+saUyoKmDyugIBfZ3xZkGsuPGTQD/zpHJYZE0PEkxbNrQkcG+ZOLeFbX1jKkqpKIDcpOho3qWuKkTRslNTOKkV1Kyrf92w148uDxBImNfVt7GmMsbO+jV0NUWIJd7v5uNIAxYXe1PZwlVOPnMXVF3yMH37l0LwObCA/kpqFGMsGtCxVWlrK7bff3unye+65R9ouiH3OYCWn9ifJtKsCe/GkhWG61YLdJF53VkNRNPw+nTJFoSmcYMuu8JAsxfQlhyW9HJM0LZpa49iOk9rmnUo+dkBVHWIJg+ff3I5HV1FQMC3L3SGFm6BcXOij0O/mq4wr9tMQTrC2up7Tjx49AcFIJzULMZYNuHEmwMaNG3nppZeora3l3HPPZefOnYRCIQoLCwdrfEKMCnubnGrbDuu2NBJLmHg8Km41mlzZSabZORteVSORtIjGTWwH9FRzS0UBTVW7vP9Q6S2HJb0cs2FHM4ZpZ2riuNzt6R5dw7bdvJOSQh+appJIurujlNQrE40blBR6URRlVO8uGsmkZiHGsgEFN5Zlcc0113D//ffjOA6KovDJT36SW2+9le3bt/OXv/yFCRMmDPZYhchrA01OTScQb9kZpi1uEEsYbpPKTP8nV3aSaSZI2N6MadmYlru927YdkraDquD2g/KoXd5/pKSXY2685y3aYg6aCo7iBmO27aCqCoVBD42p5bWGlnjmvrbtoOsqSup3SRh2avfU8O4uGux2CSOV1CzEWDagnJvf/e53PPLII/zkJz/hpZdeyiTEXXXVVdi2zU033TSogxRirMpOIC4M6vg9Go7jVuGta4oRTZg4jkM8YdLYmqA05GPGxBCqqrB0XiWxhEkiaQGQXdvPdiDoa1/e6i5JtadCekNl8dwKPn/8PHRNxXEcd0eV4+D1aFSUBtyt46lxKApoWnt1YtO0M7urLNvOPOZwBW5rq+u45rZX+Omdr/Orv6/mp3e+zjW3vSLVhIXIMwOaubn//vu57LLL+NznPodlWZnL99tvPy677DJ++ctfDtoAhRiruuoVVRryU9cUw3bcrtt1TTFURSFpWigo7GmM8sPbX+X0Y+bw9ke1+L06tmO7hflSbQqsVGAQiRmECjyYlpOzPRpgw/Zm1lTX8sq7u2hsiWdq5AxXX6OTDpnBK+/sYmNNM4UBD1qq8zhAfVMsc7t0s09NBct2gxrTctA1JbPk1te6NnurP01LB4s01RRiYAYU3NTX17P//vt3ed348eMJh8N7NSgh9gVdJRBnVxJOJC0M00YBvJ5UnRpNZcuuMDff+zaGaVNW7MOrq5nKvZqqYtk2jS0JDNOmviWO36tnklQBrrntFTbvbKG1zcDBnTEpK/Kh62qnA/VQHVxVVWHFsW7NnljCpCig4tgOkbhB0nRnZFSlPXcIFHTVzTFK01VIJK1+1bUZqP40LR2sMQxXU00JoMRYNKDgZvr06bzwwgt84hOf6HTd66+/zvTp0/d6YEIMlZH4Mu/qOburUhv06/i9QWrq2kgYNmXFfkqKfJkEY29IZXdjjETSpCzkz6nc69Lwe3Xqm+N89sjZLNuvkhkTQzzz+lb+/sx6kqaFYbjNmXRNwTRt6pvjVJQGKA/5Mgdqx3G4//kNQ3Zw7Wq3kG27YwoVeGltM7Bst9Kx4ziYVvuSmWk57Khro8DvYebk4iGfbRrudgnDNUskXcnFWDWg4Ob888/nf//3fzEMg2OOOQZFUdi6dSuvvfYaf/rTn/jud7872OMUYlCMxJd5d8/5iYUTO1WpzfSJSprubiLFPXgmkpa7bJO63qMpRC2HtliSUIGv03Oapptsu2y/StriBtf+8RU+2NyIadooqRkRTVNQFRVFdbBst3N3wFdAUcDD5poWfn3vGkzT7vHgureBYsfdQi2RBHc9/gEBv47fq9PUGidp2JmlNnBndEqK/CQMC49H43PHzBnyA/FwtksYrlmikVhmE2K4DCi4OeOMM2hsbOT3v/89f/vb33Ach8svvxyPx8OXv/xlzjrrrMEepxB7bSS+zHt6zj2NUUKFXhpa4nhDKrGE1d4nKtW2AKCxJY6qkjmQ2ancEweob0mgaSoF/vaDbnYOSmssye/uf4fWtiS27aDpCqTyVkzLQVEcVEVBVVM7kFLF5CIxA69pMXFcAaribsB2TLdFQiSaZOVz1diOwwODMLOTvVvIth1WvbU908ohPYPlOO4Mjm271ZhLQz5wHBrCCe5/fgOL5lQM6ezbcLZLGI5ZopFYZhNiOA24zs3FF1/MF7/4Rd5++22am5sJhUIsXryYoqIi7rrrLs4777zBHKcQe2U4vsw7zmLMmBjq+TlbEm4BOxxq6qMYhgm4QUv2niU7FcmYhnupnto95OakOOxpiFJe7Kco6M3prfS5Y+awclU1rW1JvLpKNOEuXDmKknkG07LxprZXWzbUNscwU13DE0mLXfVRgn6daNzEMC3cjZEO722qZ9POFlRFGdRAMb1VPN0/y6ermKkt4unZptJQaoluGOvbdNW0FFIzbVmNSGdMDO31czVHEsSTJlrqd/Z51JwgZzBmiaQruRjr+hXc/Oc//+H+++8H4LOf/SxHHXUURxxxROb6N954gx//+MdUV1dLcCPyylB/mXe19FQa8rOnoY1QqthctljSIpYwCbcl8Xk1EkkTp4dd2Fm7nt08FEXB59UpKfRS1xyjOZLEMG08upZJHt5ZF+GDzY3YtkM07mA7kLTBoyuZpSnHcbeNO6nO3abpZMahqu5yWCxhoiqKu4yVmj0xLYfWtiQTxgXxedyZjMEKFLNzcTbXtLj1bxS6rP2j6yrxSJLV62oB9jp/Kh2gNkcStLYlKSrwUlLoY1YqrycddBUFPJiWTWM40Wkn294sca6truNvT39EW8ykLW6iKm4wU1rkJ+h3f+/BmCWSruRirOtzcPP4449z+eWX4/V68Xg8PPnkk9x8880cf/zxNDU1cd111/HYY4+haRr/9V//NZRjFqLfhvLLvLulp5q6CLG4QdCvZwIAgGjCrWFjmjak67jgzqWoqS1CmqaCQ87uoDTHAUUlc6DXNJVI1GD5QVOpKAlQNb2UaNzg78+sxzRtNF1BdRSc1FKXYdpomoKVStC1UgUAwQ10dE3J7FJKL445OKhKupBOexTW3Or2Qcq0Txiks/50Ls4Lq3dw+8Pv4fdpFAa9OVWbowmThmZ3Ge/BFzfw5Ktb9ip/Kh2gbt7ZQlvMwLZBVclJWs4OulqjSRxo77iudd5t1t/nT7+P3A7nFoqqkDTckgAVpQECPm1Qtr1LV3Ix1vW5iN+dd97J4sWLefnll3nllVc45ZRTuPXWW9m0aROnnXYajz76KIcffjiPPPIIV1555VCOWYh+y/4y78pAv8x7anZZUujFARpbE5lClw7QFE5gpcbhOLgHUsdtt+DY7gwLjrsU49G6/ogWF7TPYJi2TSRm8Nwb27jvufVcf+fr3HD3m8QSBqoKCgqqqqJnPVa6mzhAVq4uquK+Vl6PmiquR2actuMW20sX2FO19krB2Ty6imnZe33Wr6oKRy2bwszJxe5zZE1tRRMmtY1RkoaFx6Myrtif0z29v0X10oFF9fbmVGDTvkurLW5Qvb0505X9mgsPYXy524h00rgCpowvpNDv6dTgtD8FETu+j8pL/Giaim27Qabt2DS0xKlviQ/KtnfpSi7Guj4HN5s2beL888+nsLAQr9fL17/+dT766CO+/vWvY5omt9xyC7fddhszZ84cyvEKMSBD9WXe03KX36fj1TWShpWpIpxIWiQMtwdUehRa6iCVnWuT/lNVlUzlYU11Z1V0TSGQWqKIJkzqm2JYto3fp1Fa5EPTFCIxg6Rho2ta6iDbHixlOjmlnkTT3EvcXB6F1jYjp7px9utk2Q6KqmSCpo6VgmFwz/q76p5t2Q4NzXEsy0HTFMpDbiAw0OAiO7CwLBvHcQM0TXV/3NYQNtG4wcpV1Wza2UJTOE5ZyE/Ap+fMJnWcueqrju+joM+td+T1pHfRua/r+LIglwxC4rt0JRdjXZ+Dm7a2NiZOnJj57wkTJuA4Drqu8/DDD3P88ccPyQCFGAxD9WXevtzV+aOkAKXFPhQUmiLJVFE+Kyd/Jn0Q7RBHZFoSgDt7kh5WurGkz6u5s0It7kHe79UoDLgdwlVVcXc4paIXVVUwLTenRlHdpZa0cSV+JpQFM4GNpirYjkM0YVBalLvF3HHA61GpLA3i8+jYttOpOedQnPWnc3BmTAwRS1rsaYySNC08ukpFaTCTiwIDCy7SgYXP6844qamqyOnHU1UFw7TxezRqaiOs39rU7b85DGzmqqv3UdCnM6migAnlBVSWBSkIeDjrhP0GbUdf9usaT1o0tSaIJy1mTAwNSgAlxEjqc86N4zhoWvvabPrv3/zmNykrKxv8kQkxyLoqGqdraiYBdyBf5r3lLnhUlaICD+PLgjSFE7TFjfbrdBVVSc+aqG7fpNR1iuLuZAI3OCkIeGhNHSwLg55MNd+EYbk7iIr8mZmWdLCkKAqWbVNa5CcSMzKBVXrGpqTITZZ1AE9qhknX3IN50rCxrNyDs66plBT5KPDrgMOeBnd3F6mlqvROrYBX49CFE1mzvi6n9s3e1MRZPLcC23H4yxMfEo0ZqTYMNs2tCRSFnCTj/uZPpQMLn+b29VI7xCwK7tKdoiiYppV5LQYzX6W795EC+L0aiaT7Z0lh55pGe2M4upJLBWQxEga8FTxNun+L0WSwv8y72yIM7bMYMycVc82Fh7BlV5g3PtjN359dj9NhyURVFHRdxUi1HvDoaiYXxufR8OoaM1MzIeFIkqbWhFu3RlWoKAnkzF74PCoeXSVpWJkllkkVBe6SjmXTHHFbM5SmDpQKbqBT1xTLPH+6w3g60VlRwDAtNxE65AYypSG3cnI4kqQtbqJrKuXFfgBWPledU/tm6bxK3v6odsA1cdZW1/G7+9/JJG0nTAsFSBpWJtk2HeD0N7hIBxaO42R2kWXPpKV/f8dx+29VTS/t9d+8vwm/fXkfDWbvrOEKOKQCshgpex3cdFyXFyLfZReNG4zH6rhF2JMKUrJ7Hum6Slvc4M11e9yu1rgHYbfrtZsHk2494PfqfPHE/aiaXgpAJGpkDkBAp2q+mqYQT1qZ3lI+j7t1uLYp6m7vtm2cVKASS1oUBLwYhoVpO2gaROMmza2JzLbwNEUBn1cj6PMQTRhucUHbobk1wfyZ5aw4dm5OoLinsY1/vrCx066x6u3NvLuhHr9Ppyzk63dNnI7JtuDOtiQNO9NQsymcIFChwwACgUxgsTPszp6YNu7kiZJJoPZ6VOKGxcxJxcyZUtKnf/P+BAt9fR8NRgAyXAGHVEAWI6lfwc21115LYWEhQGY9/3/+538oKCjIuZ2iKPz5z38epCEKkd/6styV/qKPxgy8Hg3DsFL1ZdxWCZqaqiOjKBQGPGzd1UJdc4xDF01kSVVu9d3sar4P/XsjW3e5jWrTMw4e3V0+8nl1VMWtS9PU6hYMrCwNcMiCibz63i72NLoF+uqb4tip3Vnucpj72VYUJbUM5aHE8bptIRImpuVw7sn7UzWtNDMe23a45rYNnQoWehUV27axbQfLsvF6NBT6VxOnq6Tt0iK3e7qVyvtJGiatUTfg6W8gkB1YmJaNabld1t3CgU4q78atAp1+3KFY4hyKx+xouAIOqYAsRlqfg5uDDz4YaA9qurusq/8eTL/97W955ZVXuPvuu4fsOYTor56Wu9Jf9NGYQVHQg5ZQaLFstNSB07TcXUjp/km7G6M8+do2AP75rw1MGFfQ5UHn3Y31meUpSNXIUdwdWXsaopQW+bnsC0toaU3yzoZ61m9vojGc4JF/b8J2HGIJNyDAad8xlU521lW35UJ2HRu/V8OrqzS1JohEjZyxdLdrLGHYmbo6puW2d3CbfPa9Jk5XFXuzu6cn0xWVE9aAm2jmFA7c2UJbzExtB1e6bc45FPkq6cfcsKOZ9VubAKiaXsqcKV2/Nv0xnAGHVEDed+VLjlWfg5t8CCbuvPNObr755kxQJUQ+6W65a1NNC5trWogbFm2NRqovVKo/VC/blR1gV30bP7/rDa467+DMwTV9oLIth/HlQZpbk257hFRdFEVR8Hk1Hnh+A1t2hrssOJc0LJIGmdkdRUl1Cbfcojuq0l7HJh2QdJfP0l2RRMu2M0m6tk2n7dm9Jf/2VrE34CugtS1JW9zi2IOncdjiSQMOBLKDla4qFHf1BT2YS5xp726sH5Jlo+EMOKQC8r4pn3Ks9jrnZjjs2bOH73//+7z11ltSR0eMOmvW19EaTbr9oFQVNbX0058JztaowX3PfpQ5q84+UPk8GkG/J7O9Pd02YXdjlJZIAjO1dKMq7uxJQ3OcitIAJYU+4okouq5SGPDQFjcxU9WK7dTSlJracQVaj4mt3e32Se/cSi+ZdQwQekr+7UvFXgeHxtYEiqLw/Fvb+feamr36Mh2KYKU/hnLZaDgDDqmAvO/JtxyrPte5GUnvv/8+xcXFPPzwwyxevHikhyNEn9m2wyvv7UrtuHEP7OlllP6q3t7Chh3NQOe6KOktw0G/js+ruTM1jkPAp2NZNmqqIJ2uuctkTWG3m7iqgmk67uOZdmbXVmb8jkMslXC8pylGwKuxYvlcADZsb2b1ulo2bG9mxsRQl0US0zu3LMvdaeTzth/oeqqJk72UVxjQKQjqqKqSqaxsOzZ1zTF210exbYeSIh+lRb5eqxTbtpMz7v5UER5qPVW7Hmjl42xDVaW7K1IBed8y1O/dgRgVMzfLly9n+fLlIz0MIfptU00LjeE4Xl3DMG1sp+sDS1/EEiY33rOai09f2OOZcbpYYLqGTnYRQHCrCxupei0eXSOetHAst0Bfungd6eUpoDnizjrpmsr4siAba1q6nHpeOq+SPY1R6lvi+Dwapm1jW+7BTFUVdE0lmbT6tAuo41Je+kvRrYjslnd2d4fhFrjzu7MRPeWP5NOUeVp2fkJLJMGOPa1Dtmw0nNvNh3P3lxh5+ZhjNSqCGyFGq3BbEsuyKQ352N0Q3evH21kf4Sd/eo2zT9wvc6DyFCkkTSezFdxMFevze1X8Xi3VADMTtrgzH6n8l4BPJ55qDWHZ6YrITk4VZQUYV+zH59PZ0xDlrsc+wO/VKSvO3da9pzHKornjeHH1DloiyZz7l4b8lIbcmjg97QJKH+wfe2kz4dRSXrrNQ+acz3FbVli2Q2nInwlsMs/XxZdpvk2ZQ+dgy7bdPlYeXc1ptJq2t8tGwx1wDMfuL5Ef8jHHSoIbIRh4hn9v98suEKcqDGg5KpumKRiGzT1PreOLJ+7Htt2tbNsT6Zy/o0BhgQefV0sV9LNRVPfAn85/URR3iSzViDwzU9ORgzt7U6FrmYOw7dh4dffsPz1bsrsxxnOvb8/0mlJSScmOA82tcRwcvnD8PCaUFRAq8DJjYogtu8KsXldLqMBLayzJA89vYMeeVppSdXfc36vzuNI7y+hmIiz7yzQftyV3FWy1xQ3C0SR1zTG3v5Q/9+t5MJaNhjvgGI4KyGLk5WOOlQQ3Yp830OWKvtwvvRRQvb25XwnEXcm0a9Dc7dXPvL4N07RSQUC6No0bwGiKSjRuEQrm1oRRVQfLcvtTtcXMzJZvaN/R1BXDtNndGEUhFWB12EUFbr0eMyufw3EgHS+pCrS2JXn5nZ386KJP8O7Gen54+6vtsxaprekeTaMw2PXXUma2O2sWpzWWpLjI22kqPPvLNN+mzLsLtgqDXlpSPciaWuMEfAWZ6wZz2Wi4A46RTtIWQ2+4K2z3xahIKBZiqKTPoLfsDGe6aveWlNqf+2U37BysVDpVddeZamojhFMJm4qioOsaZaEA0yYU4fe6vaJ2NUQxbZvyEr/bv8pyF6j8Po0ZE0OUl/jRUsX7eksHslO1eJTUDFQ0brj5OqTr2ViZ2ypZP9DezmHrrlaeenVLzmtXUuQjnrAwDJuEYWZaQHTqS5lqm55+HXVNIWnamY7rmZt1SFjtqbkpDKzR5d7oLthSgLJif6bidCRqDFmn7nTAsWy/SuZMLZGZFLFX8rHLvMzciH3WQJcr+nK/fzy3noBPz7RO+MYZS/jpna+SMAYe4jiOg02qdkzqMjW1vRycTN8o23F7P7lLSDaxuImqKng9KlPHF3LUsqksq6rEdhx+9uc3KA35aGpJYPZxaskw3ds1hhM0tybw6BoFfj13yS3rO0xJBSS241YSfvLVrTmvXaZ1hOYumbXFjEwAlfP7Zz20qiqUFPpoDCdoiiQpU5Ru80cKgx4cB8LRJH6vjs+Te2bZ3ZT5UBUj6yk/IejTGVcaoL4pTjxpkUyNTfJURL7LtxwrCW7EPmugyxW93c+jKXywuZEf/+k1FNxdRrquYuZOMPSbZTk4HeZ/3Jo07TkutuXQGI67syaKQqjAQ1vMxLBs4kmLlkiSdzfUM29aKZblYFo2pUU+LMuhMZzo95hsBxKGRcLo8Mt1Eyc5KDS2xPF5VWIJy93ebbs1f9LBjZnaum73kAOkayp+n05RgZ3puN5d24uVz1XTFjcwLbfNRXYRwO6mzPuy5DjQ4KcvneRLinyc98n9KS705V2eSr5UoBX5J59yrEZdcPOzn/1spIcgxoiuzqAdyEypopBZrsj+Qt9ZF8EwLYqCnc+8o3GTptaEm9eiqRQFPYSjSXbVt+31eHubV8meeNE0BdtxCEcNcNwlHMtyiCcstux0l85OO2p25iAb9HtoiSTbE3WHaKx+r0prNElr1F0eSxcXTO/QSicg99bCxXEcItHcjusdv0yzk3ZLinw0heOZqfLapiilRT4My+k0Zd6XnVXAgLeV9zU/4ahlUzodFEY6sMjH7fQiv+RLjtWoC25E90b6i2+06XgGHU2YNIUTbhuD1C4dVVV5c90e/vnChswXugO0xU08niTFBb7M4zmOQ1NrPFUl2O2orSjQ2mZ0N4Qhky52B06qSrACmoNpWXh0D+G2JM++vo2J44Js2xOhrMiL1+POpgylZKqzuKYqKCiYtp1ZDmuviuwGOOnt3h2l20LoupLpuJ79ZWrbDuu3NfGnR94nEk1SURpATS1bNYUTJA0Ty3JojiQ5YGYZZxxb1amtRU9Ljnc8+j7RuDngbeUD3ZI90oFFPm6nF6I7EtyMESP9xTcaZZ9Bmz6b+uZ0YKKgKA6W5ea3PP7SZrwelcKAl4BfRwFa2xLUN8VxHCgu8IKi0Bo1SBhu8OPzuJWCE0mr24qwfZU5xqX6GPRncsX9Xdp33Fi2myvjABt2tDCpogBVUWhsTVIY8JA07EGbvemKbbk7rBJJCzu9w4vcmR7bARX330FNZSUrtAc66eF59M5LOmur6/jHc+vZuKOFSMzIBEJlxX6CPp1AhU4iaWX+Xc47+YBMd3Pofcmx0K+zbXcrfq9GZWlgwNvK+5ufMNKBRT5upxeiJxLcjAEj/cU3WmWfQdc1uWX8dc3dGm3b5BxUYwmLpOHWakkfiR2gvjmeKVhnWe1tFWzbIRxJkjCsXptj9sYBioIe4sncHUm93cdxwDYdVMUCpX2bd7r+jGU5NLUm8Hk1ykN+wpEkAZ9ONGF0uyV8b5k2hIJ6zgxRV6+OncpA9nl1gn6dlkgCNbXNHcXBsaE1msx5f6+truPGe1YTbmvvlG47EE9a7G6IUh7yu9vpU0nGzV10N++tGFk6J8jv9fUrT6srfc1PyIfAIt+20wvRGwluRrl8+OIbzRbPreC0o2Zz20PvoSjgTrKkqvR2KCDnkFu5N12UL711ObP9WXGTbOuaY4MyRseBcJuRWa7pr1Th4QxVcQM4VYWSQi9tcZOigJdLPreYSNSguTXOnx59H9N0iMYNVM3dJj7QGC2dV2PZ7i6uptbuE5d1TcVKLf3ZTntujeO4W7bd9gvg86pUlvhpbE2yclU182eWc8ej79PcGs806MxOSLZth7rmWGbbu6aq+H1apx1SvSX7phOnPZ7ut5X3pxJrX/IT8iGwyMcKtEL0ROrcjJDBauDXny8+0bXxZQUU+HUmlAVTBeG6DiI6zmZk30RV3N5Muq7sdRXi7gzW4yZNG9N0WzXYqbo1m2qaqamNsGjOOKaML6K82O24raqgKuqAA2NdU/B2sXzUUfrhbcdB05TUFnd3m7gbULjBpmU7qIpCaZEfVVUz7+8X397Btt2tgBu4pIOYjtKXJQ2LWMKkNZZ7MO6t4WPcsNC17l+PniqxDvQznw91eoaz6aYQg0FmbkbAYObHjMYzqnxLfA4VePHoGoqmEGt1k4l1XcnUc+lISSWJpI99uubmtVi2k1ddpnviAEnTLfKXdss/3uZ3D6xFS83sxJNWqgeV3bmgXi/SAWKmKnLW65J+/bJvZ2e9lo6TmlUq8hFuS6bygEBRHLye9m3c0P7+/mhrk7vVW2vPMdI1NTOrlvm9HXeXlqYpeHSNB57fwOI5FZn3X2/JvkUBD+PLgjS0xPH1oxLr3nzm86G0fT5WoBWiJxLcDLPBzo/Jhy++/kgnfG7d1Zr5kp8+sShnx8pwS39xb9zRTNIw3RorPQUpHa5KVww2U/VaRouOYzVMJ5PT4/WoFAV1IlETy3Yw+5mDk35sx3HbMmTLPvnP2b6eeh1t2w1iigu8+DxaZht9WchPUTB3hjL9/vb7Ul9lbuzS47h8XjdA0lSly+Wc3pJ9gX7tdNrbz3w+BBbS5VuMNhLcDKOhyI/Jhy++vspO+Mw+qL2/qYHte1Zz+dnLeg1whmLWR1UVTj9mDr/8y1tYNjjYPSbU5i5HKZlt46MpsOlN0nAbZFaW+mmOGDiOjWm5falUFTRNRddUYgkTLdWTyufVCPh0mlK7sbqS1R6qS9nLToqi4Pdq+Dwa8aSVWmrq+v39iYUTeeQ/m7AsG03NjXCyd2ONKwlkAiTbdrqd1ewt2bevO50G4zOfL4FFvlWgFaInEtwMo/4mBvblQJ4vX3y9sW2nQ8Knmjno2LZNc2ucOx59nxu/eVS3Y+1uav9zx8yhMOAdcEfvNevreOW9Xe07bPo4S6EobrG8jjMTY4Vtu7VgykN+4obN0UunsOqt7RimRXGhD9txSCRNbNvNc0kvF/m8Go0t7fWCMq0iVNBVFQc3SAI3SErvMnO30LuPE/DrqWUxB59PI2HamYNpV+/vudNKmTahiM01LRimjZa1jpZ+fnc2qv2z19usZk/Jvn3d6TRYycD5EljkUwVaIXoiwc0w6k9+TH/W6PPli68nG3Y05yR8pr/oFUBR3YTIbbtb2bCjOafuSFp3U/sbtjfz4z+9RsCro6pKvzt6b65poTWazJT07w8na6fUWKSqbvdvt6qvic+nsfzAqaxeX0tjOJ66jYrjOIwrbc+DKfB7CHg1apvibs8oRcGw3JkfRQEFFUVzUsX8wKvrbiKxqlJZ6gZSO+vaMEw3uHEgVVcmSLgt2e37+78+PZ8b/7aacCSBbdudlrzGFbfXpenvrGZ3Jxq97U4azJy4fAks8qUCrRA9keBmGPU1P2Z3YxsPvrCxX2v0+fLF1531XSR8pimKgqopmJbN+q1NnYKb9NR+NGZQFPS4VYIdt8BePGmmqvFaTCoPYtpOr7kM6UApGjOIp7b2jpVARUu1WRgMdmrapa45jmnb/P3pj3AcN7E34NWpLC/giCWTeeOD3cQSFrqaO6vi9+kYhkWo0ItlO9Q1xTBTy1qKkt4yb1Ne7OWUI2bz5CtbqG2KE0+aWcUU3aU/VVVpixusOHYuE8oKKEy1vohEDTZsb2bW5GIWz63g8rOWsfK5arbsDmdm1JKmhUfX0FR3KcowbVqjBrqusnhOBZtqWnr8rORTMrAEFkL0jQQ3w6hP+TETQrz8zs4BrdGPii++rHQIO/vU2uk+D3RTTQuba1qIGxZtjUbqAEtmGUnXFSzLxrAc/N6+d/QuCnpobUgOyhbrTE+k1H93rLo7HNxZkcGTXp5LN+Z0UlWDHQfihsWexiixhMknPzGTtz+qpaY2Qms0CSiUFfs5YEYZ/1lbg0dT8XkUKkoDmfYWdqqekKaqfOqwmZx+9BxmTQpxw91vZvJuALwejdIiP36fRl1TjKde3coJH5/Gq//aTU1d18FGxyC/NZbkgec3ZGY1bcfd1WYnbR74VzUPvrCB8eVBzvnk/iytqsx5DdKBcCTVUTzgd2cIR1My8GiRb7soxegmwc0Q6/iBPf2YOfzu/ne6zY/5xKKJ/OO56jFXCbRqemmmQJuiOJnZl2y6plA1vfOS1Jr1dbRGk5lcHbd4XnsbAtV2AwnLtgGtzx2922KDV4nX7tALSU2tcO1l54V+cavnDl1IpalKZhnK3fZuE40bvP1RLddceAjPvL6VJ1/dSmM4TlM4zn/e2ZnTgyu7/YFtO5i2m7i9ZK4bUBQG3N1RwVQAoakqPo9KLGmxqz5K0jDZVNPC//3zXVRVoTTkTzW/7Dyr2fGzkZ6hWVNdy2MvbSYWNzHt9vdheHszP/nTa3zxpP05/eg5QHueWF1TFMdxiMZN93XQVEJBD7G4OWqSgfOdtI8Rg02Cm0HS1VnHuxvru/zAnnTojMyZbsf8Acty+r1GP9AznuE8U5ozpYRpE4rYlEr47JpCWzy3HL5tO7zy3i63oq6iZM7oU5kTQLqWipvLk9ZdLkM6B0JXdSKxwW1omV3LZTiDmuFiWA6edM8n1V1SCQW91NRGeOb1rfwztZTq87ozFG51YYeG5ji6plLg96Dg5s84jkND2MyZtQi3uV3JQwXezPswmjCpa4rlbM13k9AdmlsTeHSVoE/HE1Kpa4rxp0fe59IVi5kzpSTnvayqCrMmF/Pnxz8gFncLA7r1dNylLwcwDJu/PvkhsyaFWFJVyZOvbmHzzjA47u9spbb6W7ZFfYuFR1fZvLNlyJKB95WZDGkfI4aCBDeDoKuzjlChl6bWBLbldPrA7mmMcsnnFnW5w2fD9uZ+rdEP9IynL/fr7cu1q+uBLu+jqgoXfOoAfvjH1zC7OPJrqoLP27mo2qaaFhrDcby6lrpf50Im6WRgX1ZJ/O5yGdI5ENGEOSZybAZCV8HKKkLYH6Zt41HcnW62484mGobJk69udYMTyyYSszNLh6qiYDluvo1WpuD1aBimTTiaxKOrLJ47LpPz0jE/xQGawolMzy8rs5SloKkKpuXQFE5AETS1ut2+N+9s4ce3v8aMSZ0Dh001LezY04qZSjbO3lquoKBpNoZpc/cT61gwaxxPvboVx3Gfz7KcnHeeg/sea21Lsqa6ts99pDbsaGb91ibAnc2cM6Xr++0rMxnSPkYMFQlu9lJXZx1J02LrrjC27TC+PIjP4wYp2R/Y+5/fwA+/cihAZjtyqMDLjImhPq/RD/SMpy/3A3r8cu0uoAMIR5Jd3qcw4KXArxNPmpiWg+04KLizLGXFAfQuiqqlD5ilxT4amuOdElLTB+iCgKfTTpjpE4qwHYfV62ozgVY6B6J6e/PwJ8UMo8zrY3f+NfcmpsvJK1Lcbfym5bCzLpIqAKhkZnbSFYqVVMwZiZkocTdZ2HbcxN6HX9zE4y9tYXJlIacfMyfnvZ8w3Eah6Tyf9OyNe4xznyNpWNSmmp625xw5bN7Z0ukzEG5zG5malp3TLT1NTSUc72lo499ramhsiaMqSmapL/vmSup1cIBX3t3F6Uf3vrTU3Uxux4BlX5rJyIe+WWJskuBmL3R31pE9s9DcmiSYmo6H3A/sk69u4ZV3d3X6sls6r5I9jdEe1+ht2+Guxz8k3JagpNCH16Oh0PsZT1/OlO549H3aYgbxhEVRgQdd1YkmTKq3N/Orv6/m04fP4qlXt+Z8+bbGDDan+leVl3SdC+EWgFMoCfloDicxLAtSu5QaW+IE/BpJw+bNdXsAcs7mPZraKSE1PSugKODR1PadMDEDTVGIxAx+9uc3Oh1IViyfy6/+vpq2QV6WygehoIdI3M0lGqqigpZtp2Y6FBrC8fYu6rg7qVQU938KqJpb+RhF4Yzj5pJM2jz28mZM0069d9TM++R397/DSYfOyLz30/+mqkbm39tdQlIyDUot2w2Qs7uet0YNvB4V03JyPgOhAm+m6GJXfacyieoO1DXFALfPVWa5MStbPP1XXVVpbIn3evDta8Cyr81kjMb2MWJ0kMaZe6G7sw4rNe3t1gmxSCStnPt5dLey673PfMSWnWH8Po2SQi+qCtXbm3n4xQ0cuF8lZSE/rVGDxnCceNJixsQQl6RmVa669d9s2N5MLG6ypzHKzro2ogk34bGnhpm9nSkVBjxs291KJGpQXuzDshx2N0ZpaIkTT5g0tMS5+/EPCUeSlBf78Hk0FFUhEjVSj+Fuz1UU8Hk0ykM+Ygk38bIw6MF2nNQMjNu4MX1WnjAsmluTROMmf3/6I6794ytcc9srRGLJTCPDgFdjUkUBE8oLGF8WZEJ5kIKAh8KAB8t2aGpNEE9alBf7cRRoaI7j86oEfBqGabFuaxM3/e0tbMfhss8vpTDQ9RfqaBZPWkM+I+UGTg6G4b7Pg/721zHdbsGy7UzSd3pGpMDvYe2GOkzTzrx3VFXJeZ+8/VEtl3xuETMmhjJNGh3bLcBXURrA63EDHsdp7+PlkBvIuTM6NomkyeaalsxnYMbEEMVF3lTH9/S8S2bkqeUvDZ9Xo6I0gEfXcppVOl08V3GBu829p4Nvx4Clq9975arqzDLvvtQIVxpyiqEiMzd7obuzDrdIHe4Znk1OMiS4XZkTqfoq48sCxBIW9c2xTMG0thg89tJmCvwedF2lLBTghI9PY+7UUt6pruexl93dHiipfjwoJA2LuqYYFaUBN8EydcbTHEmwYXtzJgemOZLo8UzJdhwsy8Zf6CWWcB/TTiVUqqlEC8t2iCdNYgmLoN/d/WKYVqYqrGHaJAwbvzd351J6C65lOXh0pdvdPY7j0BZz65f8tsPZfFHAgzdrJitU4OUzR87CSX03zplWwl+fWEdDszsTVNsUcw/CqceOJ0yuv/N1rr7gY1x13kFcd8frbkAwRiSHKY/IdtyaOpWlgVTicPssmEP637X931ZTFaIxo08H7sKAlx9+5VA27Gjm1pVrqW2KUlnidgFXFIW6phiWbXdK2lYAXVfdGR7VwbJt2uLuZyC9jNrcmsi870zLcYOXVFkBRXFPPKZUFnLEksk89O+N1LfEun0NVDW1K85Rejz49idg2ddmMmSrvBgqEtzshe4KdPk8bjGzRNJKbV/O/cC2RBIAlBTlBhDQ3h3ZcSCWMCjzB6htjPKnR97H79WIxt1Ghh5NJZ0KqSoKqkYmwTJQobuBkuPwt6fX0RROZJZmSkN+d/mmu4TlVCDg1VWaWuOpyrFZiZeKCtg4jkNTa5yAryB1Jp3+ond/h/S2bGj/Qt6wrRlVcZcz0jk3XdFSyxGmZROLm7y9rpbPHjmLp17bRmM47h7ENJXyYj8A96/akPX7+djTGMXrUaltjOXsYEqLJSx+8Ze3uPLcg/jBlz7Oz+96g9bo6Fiiys4zGmnFqQO6bfc+Lttx2NkQ6fOBW1UVqqaVcsGnD+Dme99mT1OcQr9O0K9TUuSlsTVBdvCkKO57IrObTlFS/aPg/U31vPTOLmJxk+JCH5qmuonIgGHZqAruLI3uzhBkb83uqV6RbUNdS5zCgIdIrPtgoz8BS741wh3qHVuyVV4MFQlu9kJ3Zx2KolBS5GNPQzRzWXY+iEfXcBw3T2R3S9SdGVHcrtLZbAeaW91S8nZqo1A62EjX53AcB0Vzn9vdnmsRT5i0RJIkTYs9DVFChd7MGn9tY5RY0sQMO0woC3Q6U4onTXTNbYdgmNmJl+4Sg+O0J1emZ2gy22lTw1eU3G3Z6S9kcL/MKkoC1DfHsHuoyaKmfkdFgfc3N7BlV5j0TqnSYj/zZ5Xzxge72/OCNA/RuMHW3a0kkhZeXc0ENkrm/9rHGIkZ/OO59Vz75UP58qkL+P0/38Ew7SGtE7O3FMjsEurquuEeeWM4kQp8u3/+dG6Mqip8tLUJrYcDd9J0A+uddRFCBV4iqeJ7hukuMcXiJqrqLncGvO7uuXiy65kqBydTDPCVd3fl5LD4PBp+r05jS4ykabuzJwVepmQl927Y3uwuvZb43c+S0fXzpIs3/vb+d7pN9O1PwJJPMxnDtWNrNLSPEaOPBDd7oaezjljCorTIT0nIRziSpC3rA/uJhW6hvvR25PRZidPh1De7JYCuKW7wk8ph0HS37096R4obSzipZocJDNPGo2mMK/HnJiUWq5iNNknTor4lTijozTlTKgx6qSzT2d0QdWdjUgXzOhbds2xQFXfqP+j34NE1EkkTRXGryqa3ZWd/IacL+RmWTU8xhG2nEjktN7hzANux8Xo04nGT7XvCbNsdxutRKSvy0xY3iLQZmKkGjNC+PJMd2KT/mn7qjTuauerWf9MYTpA07C5nefJJ+3JPrpGczdFUN8HX6SK00VI7pjRVpaTIS1M4QVlxgNqmaKcDd1vcoK4phqIo3Pus2+YhljTx6BplIR9lIT/RuEFLJEksaRHw6hQX+kg2xzMzh+573m3MmX6dbMdhZ10bXo9GLKkT9KX7X+kEfYVEogbxpMV5n9yfo5ZNyXwW07MtpUU+igIeGsJxWiKdZ/dKCv2UFHl7TPTtT8CSLzMZw71jK9/bx4jRR4KbvdTbWUdXH1iAl9/dRfX25kwA0duBNdN0UEnPXyhuR2XbPaNyEzjd6wr8HtriJqUhH12t8ZcW+QhHk4wvC9IUTnQaM8Cv/r6aaNxI5djkjiVdidd23CWegFenIKATT5g4jntmnV76aokk8OgahyyY4M46aQq7G7rPY4D2/IfsZatwmwHkHlxMyyKWaOvxwN6xKk7mpo5DW8ykpraN0mIfqjp6C++N5DKVYbnNL7uauVEUBW+qy7ffq9HUmuDQBRN5+vWtOQfu1phBQ7P7nigv8VHo97CzIYphuDlolu3F51EoDHppiSRxbAfbsSnw64Q97vJv+uOTnZia3kVnWO6W8uyctPT4CgIekqZNcaEv50Canm1pjSWJRI2cmRsFUFR391bAr/e6Zbm/ActIz2SM1I6tUdE+RowaEtwMgt7OOrr6wKa3I0fjBpbZx6OTQ6qYmVs7xF0OUCgLuduuG8NxNFUhEnXPbi3LpqzYn/kyd4BE0q3zYdvwhePmURrydznmyz6/lBvufjMnUTSd16BAascJRKJJzFSCtKYp2A60tCUJtyVTS2XuDNPtD7+fOlD1/rt2N0PR5W37emB32gOddM4PQMCnEWkz3O3Kot+6SgpXgNKQ227BnXVzE8Q1VWFJVQVzp5XkHLgjMcNdriwNUOD3EE+9d3XdzZlJ55Gl37ua5r6nkqZDaZHfnfFJ7X6ybPf5VRVUxa291NyayPQiSz9W+pDcXQ7LrMnFhAq9bK5pSZ1UZM224G4U0D1u4UnoPdG3vwHLSM5kSO0ZMRZIcDNI+nLW0TE571OHzeSuxz/s83M4QFGBl3DEcAMcxb00nrRoTiUpFxV4CPh0Eo2xnB1UQFaNGPdgdOfj73PMgVNZMrey0xfnwlnjOHj/8fzrrR2pCsBu4rKDO8ukqSplqR1Vqqqgawq2reBYVmYGCcfB51XcJR+rq4WL4ZGbC6TkBFgNLfGxXMtvZKSC7oZwPLN0ajsOhQEP4WiCUNDHZ46cTWtbkkg0yX3PVVMY1PF73a+jTCmFrDyydD8qO7UEa9sQT5oUF3jd+ket8dTsipNZGi0t8hPwabTF3JkXN0/Mfax0C4i+5LB0Vxcn+8q+JPr2FrB0lbyb/Z1i207OzsehCnb2tR1bYmyS4GaYdEzO0zSVRNJEVxWMPs5SpHMriou8bpG9pIWqKDRH3O2tCu4XU/pM2N12bdPQHM9sw05/STvAtt0R7n58HQ8WbGTmpOLMGeQD/9rAP579iEjMzDx3emuvmkoOLQi4S1HNkQS6rWKYFo7jztRkZ2BE43an2ZKRkg5ssoOdfNl5NJY4DjSE45n3SrqFgWHaXHfH6wR8OqqioGsqRQVeTMvGq7cn2qZLKTikqyC7B/ak6QbOdmrnflM4QVvMoLTIz6RxBTS0JGiOJCgv9hMq8GZmHdKzO7Zjp2aabBJJesxh2VTTkkooDhCJGqnqy650kGzbDgnDxudR+5zo291JUG/Ju8PZjiHfdmwJMRAS3AyDrpLz2uIGkaiRWSrpyzHWcdwv9PRGpKBPx3Hc5oKqkt6Cnc7fad/VlK6po6mQ/o5WAI/u9syJJyy27HQTBRfNHcczr26lu9Uj9+Di0BxJuvkPgGVZqWdzMgeenHHTudbPSOgYXElgM/iyA8b0nz6vu4W7JZLAshwULCZVFGBaNnVNUaIJk9aY2zkc2ksppGdbFAWSplvkMVu6WF9dU4xxpX4s28ajq/hS9ZXSgn6ditIADS3uTFJbzMTv1XrMYclOKA4VeEkkLWJxk5a2RM7vF0+YRGLOXiX69pa8e9KhM3jylS3DltybTzu2hBgoCW6GWFfJeW5A0D670d9jrOOAoirEk2b7lHY3D9Jxh1PmclIl7VUlteNJoyWS4OlXtvZ5PP0Zfx7ENmI4OOneT1BS5CPo9+D1qOyqb8ssKaXLDPhTlYBju1tpSiXapgv1lRb5qW2Kpgo+qkSiBrZtZ04E0m0UwG3mWdcUY1xJgMqyIA0tcbdydtZBOeDT8Ps0pk8s4qwT9qOk0Nfjsk7H2Qu/1/3x+TSawm6TTjuVb9RVk86+6jV5tyXByufWoypK552PQ5Tcmy87tsQoFI1CTQ2EwzB/Pvj9IzYUCW6GWMfkvGjCpCmcyMym9JeDOyXu1RWiib4l6HYn+74766MDfhwh0hzIFHQM+j34vRrNkQTxRGp2L/V/exqjFBe6ZQjcXVAJapvjlBb68Oiq2yXeo5FU3FIJCcNCVdxZIFVViMYNslaKsGyHgw+YwMfnT+j2oFzg93DeyQf0KQjpbvYi6NPxj9NSuWxBLl2xmDlTSgZ8oO8tedfrValvSlJR6h/W5N6R3rEl8lA4DDt2uD81Ne1/z/5pbGy//bJl8MYbkFXzbDhJcDNYHngAvvtdGD8eTj4ZPvUpWLgwJzkvmjDdtX/bzV0Z6M7jdOl4IfKVY7uJwdGEu0Op47vVMG3qm+OpVgnuTExR0Es8aRGJGalZHiXVFyxVsyiVuGtaHbZlp5bCnn9rO1MqC/na5xbxwPMb9uqg3NvsRVHQy5dOmU/VtNK9ep16S951k/jTrUI7G8rkXqk9s49wHGhq6jpYyf5pbe3f4779tnuf4pFZvpTgZrD8+MdQXe3+/Oc/cPXVMGUKs488loOcGWza/yCaYkqqOZ+SWqaRAEWMTQ5Q2xjNJLZ3x3YcdNzu7oZhceZxVbTFzJzO4bblsKshmtOvKn14Tc8Ugduw9Y8Pvcf8meV8bvkcgn4P67c2AVA1vZQ5U0r69TsMx+xFb8m7tuMGNt3tNRzq5F6pPTPK2TbU1/ceuMR6rj3WL6oKU6bAN785YoENSHAzeD7zGVizJveyHTsovufPXAYYms77U+azevZBrJ79MXaXTRyJUQoxbNqT23u7nYPPo2FaDi+/s8utMJzqHK4oCrbeHtT0RFPBtm021jRz09/fdotVRpJ7tbtoqGcvekveTSZtt0Bm0qIw4Ehyr2hnWbB7d89BS00NGIPYN8/jgcmT3eClu5/x40Ef+dBCcTrW/B/jjj32WACee+65wX/w1avh0UfhscfctcYeXtqdJRN5c9aBvDnjQN6dsgBT73paWoh9wbhiP36fTiRmgOPWa/J53JmMeNJiZ33E7a/WA3eJSqGk0EtzJIGqKkwoD+LVNQzLpjVqEPDpg767qCv9aTiZ2S2VMLtM3j0xvVuqm+svGYbfRwyzZBJ27eo5cNm1yw1wBovf33PQMmUKVFSMWA4N9O/4LcHNUKmthSefhMcfh6eegubmbm8a9/hYM20xb808kDdnLqO+SL6oxL7Ho6vomptMXFkayAQD0bjJnkY34b0vM0EeXcWy7FRwU4DPq5FIVT2OxAxmTy7hhxcdOmS5Ix1r0jhAWcjPSYdM58RDZnT5vPlU50YMsVis+4Tc9E9t7eDWqigshKlT3QClu5mXsrIeqlXmBwluejBswU0208R+6WX+89P/Y8aal5hWu6XHm28eN503Zx7EmzOXsW7SfthdrMULMRYpuE0tS4v9OTM3uxvaMgnFfaGmigAWBDwYho1pWaQrSqmqyoWnzmfetLJBX2rKrlnj8ai0tiVTVcHd8g0zJ4X4r0/P7zIg6W22pz+zQWKERCK957c0NAzuc5aW9j7jEgoN7nOOEAluejAiwU1K+osvWLuTj219m4XrX6dq/Wp8Rrzb+0R8BayesZQ3Zx7IWzOWEQ7K2roY2wr8GrquMa7Yn6kLtbOujUTSHHC9pHS/KbcVhBv8BHw6Xo82aLMgtu1wzW2vsGVnmIBfoz7VsdwNQBwsy/37uJIAXz9jicy4jCa27c6m1NS0/6RzWrJ/wuHBfd6Kip6DlsmToaBgcJ8zj/Xn+D3yWT/7kPbdF0GeLZvAk4tORDeSTK9ew4Gb3uTAjW8wsXl3zn0KE20c+dF/OPKj/2CjsH7CXN6aeSBvzVjKxvGzZVZHjCkKbvCha0rOFuzCoId4wuz1/t1xe6K1/7ftQFvcJGnalBT5BqXab7pmTWGBx215ktoZmcoGAs3BcRwiMWNIumoPtn1mpii9TNRT4LJrF5gDf/91oigwYULPgcukSSNaBG+0k+BmmHXcfbGzLsJ9Hi87lh7K7U0xKut3cNDmtzho01ss2PE+Hrv9A6XisN/u9ey3ez1ffOVvhP1FrJm+mNXTl/L2jKU0FpaN4G8mxN5zcJeh0tWNw5Fkpu6NpqnYtj2o1a4N06axJU5lWYBYwtyroCNTsybVa819jPbHSQdufo+W9121x0SOj+O426B7C1yamgb3eTWt9x1FEya4O4/EkJHgZgRk145I17nwaCoVpQF225N5uHQyDy87lUAyxuJtazlw81sctHk14yK5a7WheGtmVgdgS/k03p6xlNUzlvL+5AMwdGlsJ0YfBbdmDSiccexcKkqD/O3pj6htjFIW8lFT1zbgCt9dsWy30OC4ksBeBR2ZmjWGnanSnC3dCNTn0YgmzLztqt1br6vh2G3Wq0QCdu7sOXDZudPddTSIrIICGovGURcspbGonObQOJzJk1lwxCJmHnyAG9SMH+8GOGJESXAzwrLrXJSHfIwvL6C2KYpjOyR8AV6dcwivzjkEHIcZ9Vs4eNObLNvyNvvvXIfm5CZXzmjYxoyGbZz21kMkdC/vTlnA6hnurM6O0sl5nwkvBLgtFipKAzSGE7z87i7O/eT+NIXjFBV4UFWFUIGXuuZBLDoGbtKv42Badk7Q0Z+lmfRneeOOZsDBcZTMR85xHGzbwetxO55nF97Lp+WfXntdDUEvqxzparkdg5aOgUt9/eA+b3qZaPLkbn/ejfu45clNWUGfmikx8EiLzqUlM1g8aZTMau0DJLgZYV2Vea8scb/Yk9nNcxSFLRUz2VIxk398/AwCiSiLt7/D0i1rWLb1bSa07Ml5XJ+Z5KAtqzloy2oAaosqWD1jCW9PX8raaYto8xcO568pRJ8oCpSGfKiKQmHAw5ZdYZ57fRvxpElh0J3G9+hqTvfxweA4YBi51X77uzST/VmOJUwsy0bRFByUVMsVhZIiH5G4mSm8l2/LP731utqrXlZtbe5sSvonPbvS8Wcwq+UCBIM9Bi2ZZaIeCs/ZtsN9t70yckGf6DcJbvJAxzLvpmVTFPRQVlxE0K+zZn3ns5SYL5gzqzOxeRcHblnNsi1rWLj9XfxmIuf2la11nPTuM5z07jNYispHE6t4e7q7hLVBEpNFnggFvQR9OtGESWNLnKRh8fzq7SSSNkmjjfJiP5btZLqDp/8ciI73jSdNZk8pyQQdA1maSX+W73j0fTbvDGNYbh85byopOpawCPh0PrFwIg88vyGnzUQ+LP/01uuqy15W6YJzHYOUjsFLS8vgD7iysutgJfu/i4v3etZ6SIM+MSQkuMkT3ZV5f2H1DtZW1/d8lqoo7CqdxKOlk3h06afRTYMDdn7I0i1vs2zr28yq25Jzc82xOWDnOg7YuY4vvvI3Ir4g701ZwNppi1gzbTE7yqbIEpYYEUUFXloiSRrD7duoS4t8NIQTJJIWO+vb3H5Vqc9D+mMxkCAn+/aKAoVBLyuWzwXYq6WZxXMruPGbR/Hkq1t46tWtNLbESYdi5cU+AO57dj3NkUSm9UQwoOPzKCM+E5Dd68oPFEZaKA7XU9xST6ilnoKmegqb65j3ogmNdW7QUlc3+APx+boPVtI/EyeCd3jyCgcU9IkRJcFNHumqSd0RSyZz20Pv0hrte38QU/fwzrRFvDNtEX/mfEramli6dQ3LtrzN0q1rKI7l1mIoTEQ5ZOPrHLLxdQAaCkp5JxXorJ22iIaicXv9uwnRG4+u0tASJ54wM4GH4zg4DhQGPCSS7jKtbTtoau7WbkUFTVFSzTXb6ZqCz6MRNywsy0FR3Bo32fdVFZgxqThTXG/D9uZ+naV3lzNz8idmctIhMzLX7W5s45//2kA8YeHzqjiAprqFCeuaYlSUBgj69KGfCUgk3J5Eu3Z1+nP2rt386N1qgo11FLc1ofXW86K/VNVdApo0yQ1QJk1q/8n+7zyrlttbg9OhbmAq+k+Cmzyn6yorjq3irsc+6FPp+a40F5Ty/AHH8PwBx6A4NrNqN7Fsy9ss27KGebs+ytluDlDe1sQxH77AMR++AMCO0kmsTQU670xdKPk6YkhYlp1pV5CtrjmOqqaqDjupTuAoqIqTmcFxKwC7O5FUFfxenWMPnsr8WeMoKfQRiSW587EP2La71c2FUdzAoqI0yGePms1JWW0R+nOW3lvOTPqExS3w5wY25cU+ogkTHPeERlHBtNwdW4EKHYUBzAQ4jtviJR2sdBG4ZP7sYeuzAkzp2zN2Nm5c98FK+r8rK0flTqLeGpxKA9P8I8HNKHD60XMA+OuTH5I09u5MylFUNo6fw8bxc/jHx8/AZ8SZv+MDlmxby6Jt7zC7bnOn+0xp2smUpp18au0T2ChsHD87E+h8OGk/4t7AXo1J7Bu6mjVRSM26qCpm1hXubd3dRpZtY1ruLIyCu3W7pNBL0O/BMEzqW+JYthusBH0qFWUFHLpwIkvmVubsPLpxTgUbdjSzfqt7cK+aXsqcKSWdln36epa+p7GNf76wsU95OR1zNjQ1lRQNKCioqrtjK5G08Hs1DNPGo0BJMgIfNrhLP7W17k9dHezZ0zlwSSQ6jXUwxLwBmkPlRMvHUzpvJmX7zeocvEyc6C4ljVFdbfzo2MB0xfK5kkycR0ZF+wXbtvnNb37DP/7xD8LhMAceeCDXXHMN06dP7/djjWT7hb1lmjY3/m01r7yzE3MwK5llCUVbWLT9XRZve4fF29YyscMurI4sRWXD+Nm8N2U+70+ZzweT9peZHdGt9E4n03SL8WUvL7ldvem2SJ+mgqqqOI7DhPICYgmTpnA85/aqAn6fjm8v2ipk2iikyjN0PEtvCCeYPqEIBcW9TXHXt5kxMcQPv+I26Fy9rpZf/e0tJnpMQtEwBa2NGLt2E2xtoTTWQnG0haK2FiqMVoqjLQTDTRTFwmj2IHZ97qiszA1KJkxw/8z++4QJ2BMnsdkJ0qL4RnyLer7It91t+5ox11vqN7/5Dffccw/XX38948eP5xe/+AXbt2/n0UcfxdvPhLLRHNykmabNX5/8kEf+s4mEYQ/6tthslS17UoGO+1MS63nHg43C5ooZvD9lPu9PPoD3psyXfliiVwqg6wqOQ07eTMdEYVUBn1cj4NNpDHc/U1EW8hH06zRHknh1lc8fPy9n6ak3md1SCbPLs/TPHDWblc9VE1KSlCUiFLU2URhpprCtmcLWZgItjQRbm1hcAgXhRozde6C2Do/V99y5AfF43ABlwgRaQuWsT/rYqRXRWFBCa6gc77QpHPnJAzngkAPG9EzLUMqnukT7mjEV3CSTSQ455BC+853vcNZZZwEQDoc54ogj+OlPf8qnPvWpfj3eWAhu0tZW13HHo++zbXcrptk5V2GwKY7N9PptmUDngJoPKEhGe73ftrIpfDD5ANZNnEf1hLnsKJssW89FJ+0F77Iuo/MuqAnlAWobYz22YVAU8OoKlu0uY+mayvyZ5aw4toczbMdxtyvX10N9PZve2cialz7A3F1LMNJMcSxMpRlhKjG0xnqorcVnDM1SUA5dd3NVKircP9N/7zjbMnGi2yFaVbvYyt5ecC7g0/OjyrAQ/TSmGmeuW7eOtrY2DjnkkMxloVCIAw44gDfeeKPfwc1Ykt5yms4jWFNdy2vv97yMtDccRWVLxQy2VMzgoQNPRbUtZtRvZcGO95i/4wMW7HifULy10/2mNe5gWuMOTnr3aQCiHj8bxs9h/YS5VE+Yy+aKmewprpSAZx/X1WlWx4sUxW142VVgo6T+z3FSP7EE44wIhW0thKItjFvfxgePRRk/3ccEO5YJYqira/97VnPEWamfwWYrKpGCEJHCUlqDxTQGQkQKS5i0/wwqqmYwrmo66visIKakpF87h0a8yrAQeSDvg5vdu90u2RMnTsy5vLKykl27do3EkPKKqipUTSulalopnz5iFg88X82fH/+Awd7B2RVb1dhUOYtNlbN4eNmpKI7N1IYdzK95PxXsvEd5W+edGUEjzqId77Fox3uZyxK6l23lU9lWPo2t46azZdx0tpZPc5uB5tGWUDH8NAUm+SyKExGSe+oJJSL4o60UxiOEEq2EYq0URcMUx8KEsn4CRnzYxtjqL6I5WEw4GCJeXEZToBhtfCVHnbQMdfz4zKzLe60q962uY0d9rFPOxvxBmkmRgnNCjILgJpYqxd0xt8bn89EyFBUvR7nTj5nLzMnF3P34B+ysayNp2BjWMEQ6uDM728ZNY9u4aTyx+JOpysm7WbDjPebtWk/V7mqmNWzr1BML3HYRc/dsZO6ejTmXR70BdhePZ3fxBPYUj2dXyQR2F09gd/F46kIVmN1s1xV5xnHwmQkKElGCiTYKE20Uxd0Axf2zjcJ4K0XxCEXxCAWpP4virRQm2lC7eM8MGVWF8nJ3a/O4cW5gkvq7XVHByvda2JTwkigtY6sZoMVfiOPxoOBgWm7hwYrSIJeuWIzaIWBZABxw8LwhzdmQgnNCjILgxu/3A27uTfrvAIlEgkBAtiB3ZWlVJYvnVGS+QAuDHqq3N3H7w+9jmMN4kFAUdpVOZFfpRJ5ZeDwAPiPO7D0bqdpdTdXuambv2ciElj2o3WQMBZMxZtVt6VRlGdzk5fqicdSGKmkoLKOpoJTGglKaC0poLHD/u9VfSMRfKB3S94JqW/jMJIFElIKkG5wUJKIEk1EKEm0Ee73cvUwfyp0/PYh6A7QGQ8SLSokUFNPkK2Tu0rmMr5qWCVzssnK2236afUUUTKxg1rSyTgGHbTu8sHoHDze+h9+nURjw4ElYaK1xjNTuL0VRUBSF046a3W1OS1fFOgeTFJwTYhQEN+nlqNraWqZNm5a5vLa2lv3222+khpX3On6BVk0r5cSPz+Cs/32MeGIYA5wOEh4/H0yZzwdT5mcu8xlxpjbsYHrDVqbXb2N6vftneVtjj4+l4lDZWkdla+/l35Oah4i/kIivgIi/kDZfARFfITFfgKgnQMzb4Sd1WbTD5Xk5U+Q4eCwDr5nEYxmpvxt4zQR+I4HfiGd+AslY6u8J/EaMQDKOz0gQMLIuT8YIGO2X+8z8OcNv8xcSDxYR9hXS7A0S8RUSDoQIB0K0pP4MB0K0BEOE/SEigSIcvz9T/TeRtIgnLa6+4GOMT30+2rf3NmFaDejatk7be9O32VzTQjiaJBJXCLclKS3yM2lcAQnDxrJtVBTa4gbjywpG7DWSgnNCjILgZr/99qOwsJDXXnstE9yEw2E++OADzjnnnBEe3eii6yo/+K9DuO5PrxBL5s8muYTHz4YJc9gwYU7O5YWxVia27GZC824mtOxmQssexrfsYULLbsa1NnS5vNUdr2VQ1tZEWRc5QP1hqDqG7iGpeTE0HUP3YmgekroHQ8v9sVUVR0l1hVZSf0/9t5M64KiOjWrbaLaFZluoTurvjpW63EZ1LHTLSgUuSbxm9p8G3qHeXjzIDE0n4itMzaoVZWbXIv7UZb7039uva/MX4q0op6QkCEA8abGzLtLjjilwk4zLCtxmnF0d2PvSIBPI3Mbv04jE3MdNGlltE/w6oJFIWnh0bURnRaTgnBCjILjxer2cc845/PKXv6SsrIzJkyfzi1/8ggkTJnD88ceP9PBGncVzK/j+lw7lxnve6rFOSD6IBIqoDhRRPWFup+t0y6AiXM+Elt1UhOsoa2uiNOunrK2J0mgTHsvs4pEHzmObeJImQWKD+rijRVLz0OYLEvUWEPUFU38P0uZL/bc3SNRX0Pny1H3afMEBLRGqCmgxC6/fJOjXsWwbRVHwqO5Wb5/XnZWxO2y5coDm1gSqAobl5BzY+7Kr6B/PrUdBydwGRSHcZpA0rFQBQoem1jgBnztTky+zIunu5OmCc5GYga6pzJgYkoJzYp+Q98ENwGWXXYZpmvzgBz8gHo9z8MEHc/vtt/e7gJ9wLZ5bwR3/cyJ/fOhdHn95y4B7Vo0kU/Nk8nm65Th4zSSFiTYK4xEKEm0UJiLtf4+3EUjGCCajBJIxAkbM/TPpLtekL/eb+R0EdiU9wxT3+Il7/MQ8fuJeP3E99WfHyz0B4h6fe5k3/fdA6r/9JFK3He7cJSVV6MbtBN4eSGS3L1BVBdOyM4FNx9o4lu3Q2Jpgwaxyzji2qtuWCI7jZJaXNFWl0K+zdVcrKOTsPCoN+ahrimHZbiPOpGkTiRokTLtPsyLDVQRu8dwKFs4eJwXnxD4p74v4DbaxVMRvMJimzV+e/JDn39pOuC2J7Tip/j0jPbL8odpWJl8lYMTxmEk8lpmT4+KxDDymu0Skp/JfFNzCh6rjoDg2iuOgOm7bR/cyB0vVsFQ19aeGrbj/basaltJ+uaVqmJpOUvOmlsC87Uthupek5iGZWiIzdA+Ooo70y9Znag8tF3RNyfSdUlW3fs2E8gJ8HpWd9W3EE1ZmycWBnGrd2e0cNBV+fukRzJtRlnns1etq+dXfV1Na5COetGhKJQY7jns/j+amueuaSmVpICcoiCZMmsIJkoZbcycU9DJzcnGvsyIdy/drmkpZyM+hCyaypKpCgg8hejCmiviJoaXrKhd8ej7nnXxAzhneOxvq+OtT6/a6UedYYKsaUV8BUd/IJYmOZbbjBiKqqnSaRSzwe9B1lZZIAstyow7TtFO3V1FVd3kqfa/sUzUn678tGx58cSPHx83MDEZ6V1FrLElTS8IN7FPNOgEShpUJbjruPAr6dAIVOq3RJImExYWnLuCoZVN6DEw65vcYlkJTS4KGlhgbtjfx4At9C5CGkrQWEGOFBDcC6Ly7as7UEk49YjZ/eeIDHnxx06hcuhKjh4PbD6oxnMjMvti2QyRm5NwGx6EtbuD36sydWsLSeZU8/epWauoivT7HS+/sZM36OgI+ncmVhZx+zBwmVRTw7sb69qKX6aWtzHKY+/fWNgNvce7OIxyHpGEzc3JxJrDpLjjomN8TS1o0NMexbQdNdZfE4obF5p0tnTqKDxdpCinGEgluRLd0XeWCUxaweF4lv/7727REEjlNDYUYTF6PhtejphKD3ctU1Z1JsW0HbPc9edpRc1g6rzITOBwws4zv3fqfTu/NrhrKWpaNz6uyZVeY393/DjMmhrqs5p2+X2HQC477vL3tPOopOCjwezL5PSgKTeEEtu2gawrg5hNZlk1RwE8kbg57e4S+7BqTAEeMJqNnYV6MmKVVlfz3WcuYP6scjyZvGTE04kmLAr+nPbBR3LostuNg227lX01TWLuhLme5pGpqKdMnhsgOAxTIySpWFDd/x63WrVAe8hGNG6xZX9vteFQFYgkT07I5ZP4EZkwIpXJzEsSTFjMmhrgkddBPBwdbdobx+zRKi3z4fVomOFhTXZuqGuwGb0nDyuQDgZvn5jhgOU5Oe4Th0HFWyefRUFUFn0ejPOQjlnCDLVtmb8UoIjM3ok/SOy+eenULf39mPbGEgW2DaVmSfCwGRTRukh072w7YWbMxKg6GYfPB5kaeenULn/zETPdyVeG/Pj2fn931BpGou4zV8TCsayoKuDMkto2iaPg9GuG2pLvNvIug3TBtkob785+1O5k+sYgVx85lQllBj0tOXW0pf+XdXWiaSmvMoCWSzFrmdXdcaakZKk1Vh709gvSiEmORnIaLPlNVhU9+YiaXn72MedPLKCrwEvR7KS7wMmdKMYctmogq7ygxQLqmEI13X5dIVRUU1d3h9/dn1rO2ur0y9eK5FZz7yf3x6CqqAqqikF7R8WgqairpOB1AAJkDuaap7sxQ6n7gPkeaV1cpDOps3d3Kgy9sRNPc/LT0zFFfgoPGljh+r0ZDcwzDbG9DkZ6xSfek8nnUYW+P0N6LqusPr0d3d6tJLyoxmsjMjei3nupnrK2u445H32frrrDk54h+aY32XGnZsh10TUVVHQzT6pSXctIhM3jlnV1srGmmMOAubzW0uOWELdvGth08uoZXd2/vOA4KCoUBD5GYkQowwLTsnJmfUKEXv1fH59FoCCc6PW/fGlU6mdkaVYH0R6PjJ2Qk2iNILyoxFsl5thiQ9O6qZftV5pzFLp5bwY3fPIqfXXI4kysK0WQbqRgkjuMGOF6PTnGhr1NeiqoqrDh2LoVBL7GkW/9GUdyDs2k52A4YlsWuhihtcYO4YVEQ0HEcGFfix+vRsG0nJwnZ61EpTh3UOy7RpGUHB11J1+CJxU3KU8+T/blwt7WDZTnUNseHvT1CuhdVa9SgY9mzdLA1ubJwxKsuC9EfEtyIQaeqCvNmlPG1zy2isixIqMBDaaGXgK/zWaEQ/aHgVgj2drNUkm47MGNiiKbWBIaZe7C2bTdxeU9DFF1VWXFsFQG/TixhUV7sp6TI5wYbqQTkccWBnKWmrpZoeg0OogYFfg+G6fadmjiugInjCijLJO8CjnvbytJgJkl5uKR7UQV8Og3hhLtbzXZIJC0awgnpRSVGJQluxJBJH2hmTS5B17X2L3IhBqgs5Cfo03tcKlk8t4LPHj2bhOHm73Q8JrsViBVKQj4+e+TsTDDk7mJK9azyaFSWBlMNMdt19bw9BQd7GmNEEwbNrQmiCZM9DW3sqo9iOw5lRX6mjC9k4rhCyosDhAp9/dpybdsOG7Y3s3pdLRu2N+/VbqbsoLC7HWFCjCaScyOGVMf8nHc31HP/v6o71R8RY5umKqiqgmEOfGudz6MRKvT2mpdi2w73PLkOy3Lw6ApqqhWFZduQ2m6tqgrNrQk21bTkvEebIwn+9vQ69jRGO8009vS8XTWqtB2HpGnh0TRKQ17sZpuEYZFImtQ12W43cZ+Oz+PujpoxMcScKSV9ei2GouCe9KISY4kEN2LIZVc/XlJVwdvVtWzZFc5MxUv5jLGvLOQnYVgY5sB33BQEdJJJK1M873PHzOnyQLyppoU9DVG3lUKq+o1tuwm96aDaMG2aWxO8napzk36MZfMq8egqt65c22vRvo66DJIaoowr8aMoCqUhP3VNMWzHxrJsGlviqCUBIr08bkdDWXCvY6VyIUYrCW7EsErXJPnNP9YQiRn4Pe7ZcVNrXOrljDJdVQDuiqZCJGYQS3S/zbsvmloTJA2byrIgC2aVc/+qDdTUdZ65sCwn1Scq1V/KdrpM9rVsh789/REPvbARVVVyHqPjLIyuqcyYGOp1ZiQdHGzY3kxTOEGo0JvJ2Qn6dSpKAzS1xlP1c6zMjE1fZ1z6UlNnuKsbC5GPJLgRw27x3Aq+fsaSnGn1woA3U2AtlrBk2WoU6Ou/ke1APLl3gU36+eIJk931EbbsCqMqUBryU1rky5m5OO2o2fg8GknTxjBy30sdAzLTtImrFpPKg5i2kzP78cOvHNptn6jelm662x4e9OsEfAXEExbNkQTLD5zKwjnj3MrMqSrMPZGCe0L0jQQ3YkR0tb4/Y2KILbvCvL2+lpXPVRPdyzN9MbSU1P/1FuQMZqDq9mBycGwHC2hsieM4DsUFXspDPhrCCV5+ZxeTKwvZsKMF02zvU9WxJYNbmVjBsmwMy8Hv7Tz70TFA6GuuS3e1YxzHIWHYRGJJEobFc29u4/m3tvc5Z6ZvNXWGr7qxEPlK9q6IEdOxVo6uq8yZWsIZx1bx3QsOTk27j/QoRXeczP8N73MmU0tMDu7SUkNznJ31bcQSljtzURfhE4smuQGGruXc14HMVm9NU1FQUvVz3MfsrpYN0Gv/qOyKyV1tD4/GTXbWt7Gzvo2WNrdoYDxp4fdq3T5OR32pqSMF94SQ4EbkKbdZ54HMmlzcKcDRVPfgpCjg96p4PRqhAg8lhV4kFhpew76133Fngpzci4gnLGobo7TFDOJJk8rSIJeuWMz0CUWZ94qmKvi8GmUhN8E3nZOT3ZIBcmvZpLdbv/nhHu56/MM+N5fsuD28JZKgtima2SYObh0d07Spb45jWU6fmlRKwT0h+kaCG5G30tWOv3b6IiaOK/j/7d13eFRV+gfw7713anohJBSpIUFKEpAWsAURFGyALKAUCUUFWQFBEHdRhF0bikiJ4iq4oL9dVqRZWB9csQELgsJCaDEiEEgjmbTp957fHzdzmUlmkpmQNpP38zw8rnfuzJzDcXPfnPOe80IlcErOhCDw6NIuHOOHdUewToVW4Xq0itCjTatgqlzeiFgThpPO38wA2CWGojILKkw2/N9XZwEAr865DfE3RSJIp0ZcdBDatw6BXqeSK3JLrLIkAw+t+vp/M47Zj7yiCrzw3kH8dfNhvPnxUWRdMsBkscNkFV3b4WG2x7E9vGNcKAzlVoiVNRc4yIGNwPMQeA4SYyguMwNArRXB6cA9QrxDOTekWXMU6xwxqBOyLhtw7vdiAEBCx0jEt49Adk4Jvjjwm5LbEKRToXWUHlcLK6r9hk/qj+NUXbmMgPwXfaNb+r3J4ak6Y+OOIPDILzIqicFTRt6M9Z8cR4XZDp7joFbxEHgeVpsIQeAQGapTknMdsx/R4Trs+PZXZbu1wHOoMNths4soKDYpZ9Q4eMp1Se4WA71WhRUf/BdqgQcDUFxqVoIPjuPA83JAZbFJ0HiRM+PuTB1vd3MR0lJQcEP8As9zSOgQiYQOkS7XHdP0F66WQhPGg+M46LQqaDUqWKx2x3OX1DOB4wHekasiP6ClOhZKVQlAeLAGxWXWekk+1mtViA7XKonBy2emugQDdpMNOq0ABrmQplC5A8pml1BqtEIt8DCabSirsKF1pA48z4OxyqXQynuLSy3Qx6iU2aOacl3KjTZwAEKD1LDaJWX20bHcykEODEVJgs0Or3Jm6MA9QmpGwQ3xa45p+qqHroUEqeXghgM4BggCAMgPJp7joNcKKDPRbqw6q8xjQeVDWqxrYMNzSpkDjUaF/CITREle7KprnGOxiUCVpSJ3wUCZyYpPv8m6fqKwJJ+PYxZFXCs1g+c4XL1mRGSoDnqtALWKryzPANjsIiyVycC1nZjssnNKI0CtEmC1ieAr/5tU8n44zqeK4HTgHiGeUXBD/J6nafrO7cJhsYq4UlABqfI3b61GQGSoTn6YllpQVGZWZgscuRAMgL2OD+uWgsP12YcbCUQiwrRK/aZgnRoRIXLuSF0/VA60JFisYrUlHnfBQHJ8DLJzSvDLuQJ8fuA32O0S1Cp55objAatNUpahIkOvnzDMGGAXJVisqPXkYufZxegwHpFhWhQUm2AXGTiOgTEGlSCgzCQX2KScGUJuHAU3JCB4mqaXJIbF679HTkEFIkM00GoEJb8iIlQDmygiIlSL21LaIzYyCLeltMP/sgvx5kfHYCi3NHGvmic57mCVtZsEWO1ire9xR+Dl/Bdn2soTq50DTsfaT9UlK5XAuQShHAAVz0NiTFlmqm2Jh+c5dGkXjg+/yITdLiE6XAuLTQLPy6UbeF7ebl5cZkbbVsGIidTjWokZNruECpMdOo1Qa66Lu9nF6AgdiksssNpFcAB0GgGd24ZTzkwD8ObQRRJ4KLghAcPdb+Y8z2HKyB5Y/8lxlJvtcsVnp1pBIXoNZj2U5PJA6ZPQGs882hevbfkJZUZbI/ei+VNVbpXmwCnbpiXGfJpoub492zW4sTud3+Ic2ADVTxeuul1aVRkocZX3ervEU/XUX+elI5VwveCnxSZBr5XPpOnYJhQTh3dHRIjWq4dl1dlFuyghNFiDqDAdUnu1QUpCDD10G0BDFBgl/oGCGxLw6rK7JCWhNRZN7oe3//kzTBYRQRoeVruEUqOtxZeGYIyha7twdO8UCbvI8M1Pl2DxYauUwAOShGpbsBljqLCI4Dk5wZZBzpdyLFFV/YaoMB0kxlBSYa2Meq7PJlWY7AjSebctuuqpvxxQZenoeumHchNDsE6NKSN7+PxwpCTgxtWQBUZJ80fBDWkR6vJg6ZPQGvMm9FWCIp4HwoLkGlg2uwSztW7LMf4qLFgNrVoFo8UOnufw/S9XUG60QfQhsAkP1kCUGEwWO3heTtB1nknTawVwAIxmm3yisONYYVw/iE+S5Fkii01ERIh8oF5RqWOJh4NOK6BTW++3RbsrlRCkrSxyWWqB1WaHxOQ8LF8+1x1KAm4cVGCUUHBDWoy6PFhqq4H1zdFLKDKYYJcYRIlBEhkCtbi5xSpBJUgwmW24mFsGuyhVOyW3JmoVB47j0DYmSPk7LCoxo9zElJm0MWnx+PQ/WTiZXQhJYspSEyDPqIiSnA/TIS4UYUFaXM4vg8UmQqvmERutx51926NPQqxPMyLujhMA5ABH10qoTCiWTzyObx9BD0M/QAVGCQU3hNTCXVAUf1ME4m+KwNi0bkrgExKkxvb/nMeB/11tmoY2MKtNlLdZQy5gyZi8tCRKrMbdZY5HC8dxKDdZUZ5jxZWCCui0KkSF66vlnPAch4v5ZTCUmiGKcjADp+3mESFapN/fC4wxbPnyDPKuVUBiDEaziBNZ15DQIcqnAMTTcQKO2aTQIA3S7+9Z7Ywl0nxRgVFC59QTcgOci38mdIhEXHSw8lpNj9dgnQqtIuTzUwQ/mQlwDl/EylwUiclJwe4KnDpyhRnk6tt2u6SUPDBb7eA4hvxiI746/DsqzDYlIEnuFoMFE/uic7twCAIPSZJzaYTK7f3zH+kLANiw/QTyi4wIC9EgJkIPvZfFJ91x5GV1ahMGs1VEcZkFZquITm3CMJtyM/wOFRglNHNDSD1KTWqDHfuzrlegdjqvxTk4CA9WI0ivQXiwBhabBKPZhuIyi3x2DJr/qcrX82yYSx+dSU7PFcfMjlqQl6ZEiaHcaEOb6CAUlVmr5T846oq5K7kBAC+8d7De8ykCOeG3pW2H9rTUCKDWQxdJYKDghpB6lHBTJOJaBeNqYQUA93WSeB6osIgQBBFqFa8cvx8dpoPZKkKnVaGswgqrXax1ZxYHICJUC8aA0gqLUt+J4+Tt1h5+cfX8eZzrGTPeBFm1ttHpM20igyONRl7mkjzmP3gquZF1ydBg+RSBmPDbErdD17bUSAVGAx8tSxFSj3iew5yHkxEa5H6tPyxIjamjeqJz2/Bqyx9/nNAH8TdFoMJkhcXmPrBxBC2A/M9gvRohejWC9HKla+d6RVUf/N5wfKfAc/U2e1S1H47ka1ECCgwm2EUJdlHyOv/hej6F648vBsBsFWET5Z1sdAjj9e3QF66UQqcVEBmqhe4Glu/8CS01tmw0c0NIPUvuFoPFU/pj276zyM4pVX5b7tIuDH8YlojkbjF46PaubpcJfrtSihM1PXAYwPHySbxD+7VHfrFJ+Y2c53lwjCEsWAOTRYTNx5ODeU6uqA3IOQmNwWqTkFdsQqhe7XX+g7ut20aLHcWlFtjsYuXhfhz+76szUKv4FvsQo+3Qgb3USGpGwQ0hDaC2H6rulj8kieHns/nQqIVqZ+g4JmHUKgERoRpIEnBvamd0aReufEduUQV27M+C2SoiOlw+4K7CaJMPufPCQ7d3RfaVUpy9WARbI9YUlSQGq13+jdrT61W34jvnU5isolzzSWLK7JVKxSOvyFhvh7X5Y84KbYeWBeJSI6kdBTeENBBff6g6HkatInQoNJhgtUngOQ4czyn5L6IowmS2o2v7COUB6/wd7WJCXPIrNGoB7WKCcaWgosZlJpXAITW5LWJbBSMrxwCgcQ8otNklZF8pqZZf4ylfpE9ia+QVGXGtxAKTxQ5RlCAIHCQJ4Dke0WHyTrT6mJ3w15wV2g5NWjIKbghpJhwPo1CVGlFh+soK1EzZUs7AKssWCB6TId3NGBnKLXjjo6MwWWxgTC4GyXEcGGNytXQAKoFHxvYTKKuwQmrEiuiOHCJJYjj3e7FLcOPIFyk3WqHTqKDXqcDzHC5cLUVekRH3pHbCj8evIOuSAeA4MAZo1LxS9R3ADc9O+PMR/u6W75zRdmgSyCihmJBmwvlhFKSTj//XqHkwJifgMklebplwd0KND1Tns3fib4pARIgWOq0KYcFaqFQ8GLtedFKrFhAcpIbVLqGg2AidVkBsdJBSnbuhqAS56KZGJYBzk7wsSQybPjuFgmIjjGYbikrNyC8y4prBDL1WgMlix89n8zHh7gQE61VoHaFHXHQw2rYKVgIbAEphz7rMTlTNWdGqBfA8B61aQHSYFiaLHZ/853y1Ap7NhWM7dJnRVu0kacd26HatQ2g7NAlIFNwQ0kxUfRgF6VRo2yoYcdHBaB2ph16nQo/OURgxqJNPn1tmssJitcNQboG9MslYEOQZjrhoPSpMNvAcEBOph1YtHyoYHaGD0EA/HRzVwHnH7JEol19I6Hh91mbvoQv47UopJImB53kIlefjWG0iCg1mqFW8XATVaINOo4JazUOnEarlltzI7IQvOSvNkWM7tF6rwrVSCyxWOdnaYhVxrdRC26FJQKPghpBmwt3DyHGon8kqIixIg3F3Jfj0MDp+vgAZ209AkuTt3Y6EW7tdRHGZGbnXTGAMiAzTgeM4mK0ijGY7eI5DkM59robX/eHkCuC80/Z1oHKbOWOQGINYedJfh7hQ5YA+SWL496HfwRiDSpDLMXCQ/6kS5MKZZY4lvGBNg81OeNpy7nAjs0KNhbZDk5aKcm4IaUYcDyNHAmu5yaYUlfQ1gdV5WSUuWg+TRQ5obHZJOSVYDQa9VgVB4HCloAI2p4MDnZdbeN5RuLL27+V5DpGhWhjNdlhtdjAAwVoVzJVn9zAGiJVfwnEcIkK0mHZfTyVoy84pQVGJWZ7ZAVeljIWcM2Szy4cdRoRoG+ywtkDJWaHt0KQlouCGkGamvh5GVZdVgnQq6LXBsNgkiJIESWSw2ETYRYbCYnkGh+cdwYPkkgcj8Bx4jgfHSdWKZPJOJxALAofWkUEI0qkQHqJxqahtNNvwyX/O4/erZcquo45tQjHuLtccInkmhEGj4mG1S+D4qgcSyonVUWE6pbr48IEdcPB/V6tVGb+RHU2BdIQ/bYcmLQ0FN4Q0Q/XxMHK3FZjjOOg0AgBBKWApShJEkUGtkvNaHBW/nYkiAwQGnuegglwrSuABvVYFxuRlM56Tl7d0GgEWq+i2onZSfEytQVtYsAZqlQC1moeh1ApRYsrMEatsC8dz6NUlGsvfP6Rs0RYE3m2VcW94OseGjvAnxD9RcENIgPJmWYXjePA8IAgSRAngeVYtd4XnALVagChKlcUw5ZmRiSMS0TehNUorrMgrqsCBE1eRU1CO4jKLx5kTb4I25xmTVpE6GMossNklSKxy6zjPISYyCEdO51Xbou2oMt6tQ4TXQUdt59jU1zIhIaTxUHBDSIDyZlklKlyH4lIzwoL0MJRfDyIAeaZEEOTzY6LCdBB4OZnXLslBTt+E1i6ByohBneolr8N5xsRksSM6TAfGAItNhNkmIkSnQpBOhWsG8w2XFfD2HBvKWSHEv9BuKUIClDdbgUcM6giVwEOl4pVt57GRemjV/PUinJycc6PTCNBrBVhtEtq72YFU9XydG3n4O+/ysdgkGC12cByH+PYRGJ3WDaXl1hveou3LOTb12TdCSMOjmRtCAlhtyyq9u7bCwf9dxYWrpYgO0yr5OBzHIb/YCFFk0KgFqFW8kkfTWLkmnmZMfjlXUC9lBaj2EiGBi4IbQgJcbcsq7pJmBZ6DVqOq3HItwFBDHk1deFuI0l2OTn1t0abaS4QELr8Kbp5//nmIoohXXnmlqZtCiF+pKZHX0+xOt5siMCYtHqF6Tb3mmtxoIcr62qIdKOfYEEKq84vgRhRFrFq1Cp988glGjx7d1M0hJOA0VtJsfRSirK8t2oF0jg0hxFWzTyj+9ddfMXHiROzcuRNt27Zt6uYQErAaOmm2PgtR1kdZAaq9REjgavYzN4cPH8bNN9+MjIwMzJs3r6mbQwipoxtJ4HWXo1Mfs010jg0hganZBzcTJ05s6iYQQupBXRN4a8vRudGdTHSODSGBp0mDm8uXL+Ouu+7y+PoPP/yAmBj6zYmQQFCXBN76yNHxBtVeIiSwNGlwExsbiy+++MLj61FRUY3YGkJIQ/I1gbdqjs6NnERMCGlZmjS4UavV6Nq1a1M2gRDSSHzd5dTcD9nz9qweQkjja/Y5N4SQwFFTAu/YtHgE69Q4diYfYcEaGMotzfaQvRs9q4cQ0rAouCGENCp3CbxlJiu2f5PlEixEhmkhMdbsDtlrrDwgQkjdNftzbgghgcf5TJ0Ksw0Z20/gwpVS6LQCIkO10GkF5BUZYbLYUVxqAWOuZ984cnTauSng2ZDq86weQkjD8auZmy1btjR1Ewgh9aimpOFW4TrkFplgtYu4VmJBaFDdTiKuT809D4gQIvOr4IYQElhqCxaiQrUorbCidVQQikvNTX7IHhXbJMQ/UHBDCGky3gQLPM9h4vBERIRom3xnEhXbJMQ/UHBDCGky3gYLESHaZrHMQ8U2CfEPlFBMCGkyjmChzGhrNknDNaFim4T4BwpuCCFNxh+DhfqoSE4IaVi0LEUIaVL+WJmbim0S0rxRcEMIaXL+GCxQsU1Cmi8KbgghzQIFC4SQ+kI5N4QQQggJKBTcEEIIISSgUHBDCCGEkIBCwQ0hhBBCAgoFN4QQQggJKBTcEEIIISSgUHBDCCGEkIBCwQ0hhBBCAgoFN4QQQggJKC3uhOL8/HyIooi77rqrqZtCCCGEEC9dvXoVgiB4dW+Lm7nRarVQqVpcTEcIIYT4NZVKBa1W69W9HGOMNXB7CCGEEEIaTYubuSGEEEJIYKPghhBCCCEBhYIbQgghhAQUCm4IIYQQElAouCGEEEJIQKHghhBCCCEBhYIbQgghhAQUCm4IIYQQElAouCGEEEJIQKHghhBCCCEBhYIbQgghhAQUCm4IIYQQElBaTHBjsViwfPlypKamok+fPvjjH/+Ia9eu1fiedevWITExsdofu93eSK32TJIkvP3227jtttuQnJyM9PR0/P777x7vLy4uxjPPPIP+/fujf//++POf/wyj0diILfadr33csWOH2/Gq6T3NxYYNGzB58uQa7/HHMXTmTR/9bQwNBgOWLVuG22+/HX379sXEiRPx008/ebzfH8fQ1z762xheu3YNixYtwqBBg9CnTx/MmjULWVlZHu/3xzH0tY/+NoZusRZiyZIl7O6772ZHjhxhx48fZw899BB79NFHa3zPU089xRYtWsTy8/Nd/jQHa9euZampqWz//v3s9OnTLD09nd19993MYrG4vX/SpEls3Lhx7OTJk+zAgQMsLS2NPfvss43cat/42seXX36ZTZo0qdp42e32Rm65bzZt2sQSExPZpEmTarzPH8fQwds++tsYTps2jT3wwAPsyJEj7Ndff2UrVqxgSUlJLCsry+39/jiGvvbR38Zw3LhxbPz48ezEiRMsKyuLzZ07lw0ZMoQZjUa39/vjGPraR38bQ3daRHCTm5vLunfvzr799lvlWnZ2NktISGA///yzx/cNHz6cbdq0qeEb6COLxcL69OnDPv74Y+VaSUkJS0pKYp999lm1+48dO8YSEhJcfhh9//33LDExkeXm5jZKm33lax8Zk38Ir1y5srGaeMNyc3PZ9OnTWUpKCrvnnntqfPD74xgy5lsfGfOvMbxw4QJLSEhgR48eVa5JksTuvvtu9tZbb1W73x/H0Nc+MuZfY1hUVMTmz5/Pzp07p1w7ffo0S0hIYMePH692vz+Ooa99ZMy/xtCTFrEsdfToUQDAwIEDlWudO3dGbGwsjhw54vY9JpMJFy9eRHx8fKO00RdnzpxBRUUFBg0apFwLCwtDjx493Pbnp59+QkxMDLp27apcGzBgADiOU/5umhtf+wgAZ8+ebZbj5cmpU6cQHh6O3bt3Izk5ucZ7/XEMAd/6CPjXGEZGRmLjxo3o1auXco3jODDGUFJSUu1+fxxDX/sI+N8Yvvnmm+jWrRsAoLCwEO+//z7i4uLc9sFfx9CXPgL+NYaeqJq6AY0hLy8PkZGR0Gq1Ltdbt26Nq1evun3P+fPnIUkS9u7di5deeglWqxUDBgzAwoUL0bp168Zotke5ubkAgDZt2rhc99SfvLy8avdqNBpERER47H9T87WPRUVFKCwsxJEjR7BlyxYYDAYkJydj4cKF6Ny5c6O02VdDhw7F0KFDvbrXH8cQ8K2P/jaGYWFhuOOOO1yuffnll7h48SJuvfXWavf74xj62kd/G0Nnf/7zn7Ft2zZoNBpkZGQgKCio2j3+OIbOvOmjP4+hs4CYubl8+bLb5CfHH5PJBI1GU+19Wq0WFovF7WeeP38eABAaGoq3334bK1euRFZWFqZMmQKTydSg/amN4/ur9slTf+rS/6bmax/PnTsHABAEAa+++ipWr14No9GIRx55BIWFhQ3f4Abmj2PoK38fw6NHj2Lp0qW466673AZ0gTCGtfXRn8dw6tSp2L59Ox544AHMmTMHp06dqnaPv4+hN3305zF0FhAzN7Gxsfjiiy88vv7tt9/CarVWu26xWKDX692+Z+zYsRg2bBjCw8OVa926dcMdd9yBb775BiNHjrzxhteRTqcDAFitVuV/A577o9PpPPbfXeTeHPjax0GDBuHw4cMu47V+/XqkpaXh008/xaxZsxq+0Q3IH8fQV/48hvv27cPChQuRnJyMN9980+09/j6G3vTRn8fQsQyzYsUK/PLLL9i6dStefvlll3v8fQy96aM/j6GzgJi5UavV6Nq1q8c/cXFxMBgM1f6jzM/PR1xcnMfPdR5cQA6iIiIilCWTpuKYFs3Pz3e57qk/cXFx1e61Wq0wGAyIjY1tuIbeAF/7CFQfr6CgILRv3x55eXkN08hG5I9jWBf+OIZbt27F3Llzcfvtt+O9995zCcad+fMYettHwL/G8Nq1a/jss88giqJyjed5dO3atdpYAf45hr72EfCvMfQkIIKb2txyyy2QJMkl4Ss7Oxt5eXno16+f2/e88cYbGDlyJBhjyrXLly+juLi4yROtunfvjpCQEPz3v/9VrpWWliIzM9Ntf/r374/c3FyXMwoc7+3bt2/DN7gOfO3jxx9/jIEDB8JsNivXysvLceHChSYfr/rgj2PoK38cw48//hgrVqzAo48+irfeesvtkoWDv46hL330tzHMz8/HM888g8OHDyvXbDYbMjMzXZKGHfxxDH3to7+NoUdNvFur0SxYsIANHTqUHTp0SDnnxnlbqsViYfn5+coZKidPnmS9evViy5cvZ9nZ2ezw4cPsoYceYhMmTGCSJDVVNxRvvvkmGzBgANu3b59yBszw4cOZxWJhdrud5efnM5PJxBiTt25OmDCBjR49mh0/fpwdPHiQpaWlsSVLljRxL2rmSx+vXLnC+vfvz+bOncvOnTvHTpw4wR577DE2bNgw5Z7mbPHixS7/PQbKGDqrrY/+NobZ2dmsZ8+ebM6cOdXOAyktLQ2IMfS1j/42hpIksfT0dDZixAh25MgRdvbsWTZ//nzWv39/lpOTExBj6Gsf/W0MPWkxwU1FRQV7/vnnWb9+/Vi/fv3YggULWFFRkfL6oUOHWEJCAjt06JDLtQkTJrCUlBQ2YMAA9txzzzGDwdAUza/Gbrez1157jQ0aNIilpKSwmTNnskuXLjHGGLt06RJLSEhg27dvV+4vLCxkc+fOZSkpKWzgwIHshRdeYGazuama7xVf+5iZmcnS09PZLbfcwvr27cvmzp3Lrly50lTN90nVB3+gjKEzb/roT2OYkZHBEhIS3P5ZvHhxQIxhXfroT2PIGGOlpaXshRdeYEOGDGFJSUksPT1dORMmEMaQMd/76G9j6A7HmNO6CyGEEEKIn2sROTeEEEIIaTkouCGEEEJIQKHghhBCCCEBhYIbQgghhAQUCm4IIYQQElAouCGEEEJIQKHghhBCCCH1bsOGDZg8ebLP79u5cydGjhyJ3r17Y9SoUfjyyy99/gwKbggh9crd0Vl0nBYhLcvmzZvx9ttv+/y+Xbt2YenSpRg/fjw+++wzjBw5EgsWLMDPP//s0+dQcEMIqWbJkiVITEz0+GfXrl0AgMmTJ7v8Zvavf/0Lr776qvLvpaWlWLx4MX766ad6aVdiYiLWrl3r9rXdu3cjMTGxxt/yNm/ejMTERGRlZdX6XZ9++ikSExNx+fLlOreXkJYmLy8PM2bMwJo1a9C5c2ef3ssYw5o1azB16lRMnToVHTt2xJw5czB48GCX2ljeUPl0NyGkxYiJicG6devcvtahQwcAwAsvvOByPSMjAwMGDFD+/fTp09i5cyfGjBnTcA2tNGLECKxYsQJ79uzBvffe6/aeXbt2oU+fPv5VAJAQP3Lq1CmEh4dj9+7dWL9+PXJyclxe/+abb7B27VpkZWUhNjYWo0aNwuzZs6HRaJCdnY2cnBzcf//9Lu95//33fW4HBTeEELc0Gg1SUlJqvKc5BQlarRajRo3CJ598AoPBgIiICJfXz549i8zMTPzlL39pmgYS0gIMHToUQ4cOdfvad999h6effhrPPfcchgwZgosXL2LFihX47bffsGbNGly4cAEAYDQaMX36dGRmZqJ9+/Z48sknPX6mJ7QsRQipM+dlqaFDhyInJwc7duxAYmIiPv30U0yZMgUAMGXKFJflq3379mHMmDHo3bs3hgwZgpUrV8JoNLp89uHDhzF+/HgkJydjxIgROHDgQK3tefjhh2Gz2bB3795qr+3cuRNBQUEYOXIkAHkJbcyYMUhJSUFSUhIefPBBfPHFFx4/e8mSJdV+wF6+fFnpq4PBYMCyZcswePBg9O7dG3/4wx9w8OBBl/cdOHAA48ePR58+fdC/f3/Mnj0b2dnZtfaPEH/2zjvv4OGHH8bEiRPRoUMH3HrrrVi+fDn27t2Ly5cvo7y8HACwePFi3Hffffjggw8wZMgQzJ49u9r/h2pDMzeEEI/sdnu1a4IggOO4atfXrVuHWbNmoUePHpg9eza6du0Kk8mEl156CcuWLcPAgQMBAHv27MHChQtx//33Y968ecjJycHq1auRlZWFTZs2geM4nDp1Cunp6Rg4cCDWrFmDK1euYMGCBbW2t1evXujevTt2796NCRMmKNdFUcSePXswatQoBAUF4aOPPsLKlSvx1FNPYfHixTAYDHjvvfewaNEipKSkoG3btnX6+7JYLJg6dSoKCwsxf/58tG7dGtu3b8eMGTPwt7/9Dampqbh06RKefPJJjB07FvPnz0dJSQlWr16NWbNm4auvvgLP0++cJDBlZmbixIkT2LFjh3LNsdng119/hVqtBgBMnz4do0ePBgDcfPPNyMzMxKZNm5Camur1d1FwQwhxKycnBz179qx2/emnn8bs2bOrXe/Rowc0Gg2ioqKU5SzHslV8fDzi4+PBGMOqVatw2223YdWqVcp7O3XqhMceewzffvst7rzzTrz77ruIiopCRkYGNBoNACAiIgLz58+vtd1jx47FX//6V+Tk5KBdu3YAgB9++AEFBQUYN24cAODSpUtIT0/HnDlzlPe1b98eY8aMwbFjx+oc3OzatQtnzpzBtm3bkJycDAC4/fbbMXnyZKxatQrbt2/HiRMnYDab8fjjjyM2NhYA0KZNG3z99dcwGo0ICQmp03cT0txJkoQZM2YogYuzmJgYnDlzBgCQkJDg8lp8fDz279/v03dRcEMIcSsmJgYZGRnVrjseyHWRnZ2N3NxcPP744y6zQv3790dISAh+/PFH3HnnnTh69CjuvPNOJbABgOHDh0MQhFq/44EHHsDrr7+OPXv24IknngAgL0klJCQoAceSJUsAAGVlZbhw4QIuXLigTHvbbLY69+/gwYOIiYlBz549XfqXlpaG1157DSUlJUhOToZWq8XDDz+MkSNH4o477kC/fv2QlJRU5+8lxB9069YN2dnZ6Nixo3Lt8OHD+PDDD/Hiiy+iR48eCA4OxvHjx9GvXz/lnnPnzimbGLxFwQ0hxC2NRoPevXvX62caDAYAwPLly7F8+fJqr+fn5wMASkpKEBUV5fKaSqVCZGRkrd8RERGBYcOGKcFNWVkZvv76a5dlrYsXL2LZsmU4dOgQVCoVunTpgsTERAA3diaPwWBAQUGB2xkvACgoKEB8fDy2bt2KjRs3Ytu2bdi8eTPCwsLwyCOP4Omnn6ZlKRKwZs6ciXnz5mHt2rW47777kJubiz/96U9o27YtYmJiAAAzZszA+vXrERsbi6SkJHz++ef48ccfsXnzZp++i4IbQkijCQsLAwA8++yzLlvGHcLDwwHIAUphYaHLa4wxlJSUePU9Y8eOxfTp03H69GmcPHkSjDE8+OCDAOSp8VmzZkGtVmPbtm3o0aMHVCoVsrKysHv3bo+fyXEcRFF0uVY1CTo0NBSdOnVyWXJz1r59ewBAUlIS1q1bB6vViqNHj+Kf//wn3nnnHSQmJioJz4QEmnvuuQerV6/Gu+++i3fffRfh4eFIS0vDokWLlHtmz54NvV6P1atXIy8vD127dsXatWuVnD1vUXBDCKk3VWcdqi4jdenSBdHR0bh8+TKmT5+uXC8oKMCiRYswYcIEdOjQAampqfjuu+9gMpmg1+sBAN9//73XS0aDBw9Gu3bt8O9//xsnTpzAsGHDlFmf4uJi/Pbbb1i6dKnLUtB3330HQA5+3AkODkZxcTEsFgu0Wi0A4NixYy73DBgwAPv370d0dLRL3s7GjRtx6tQprFq1Cps3b8bf//537N27FxqNBqmpqejVqxe+/PJLXL161av+EeIPXnnllWrX7r33Xo/nUDlMmzYN06ZNu6HvpuCGEFJvwsLCkJmZicOHDyMpKQmhoaEAgP379yM8PBzdu3fH/PnzsWzZMgiCgLS0NJSWlmLDhg3Iy8tTlnPmzJmDffv2Yfr06ZgxYwaKi4uxevVqZTdFbXiex+jRo7Fz507k5uZi48aNymvR0dFo164dPvroI8TFxSEsLAw//PADPvzwQwCAyWRy+5lpaWnYsmULli5dinHjxuH8+fP44IMPXAK4MWPGYOvWrZg2bRqeeOIJtGnTBgcOHMB7772HSZMmQa1WY9CgQVi1ahXmzJmDSZMmQRAE/OMf/4BGo0FaWlqd/t4JIa5ocZcQUm/S09NRWFiI6dOn4+TJk+jWrRvuu+8+fPTRR1i4cCEAYNy4cXjjjTdw7NgxPPHEE3jxxRfRvn17bNmyBTfddBMAeffU1q1bIQgC5s+fj/Xr12Px4sXKspU3xowZg5ycHMTGxmLw4MEur23YsAGxsbFYsmQJ5s2bh19++QUZGRno0qWLx1IRQ4YMweLFi3Hs2DHMnDkTn3/+OdatW+cS3Di2md9yyy14/fXXMXPmTHz11Vd45pln8NxzzwEAunfvjnfeeQfl5eVYsGABnnrqKRgMBnzwwQfo0qWLT3/fhBD3OEYV7QghhBASQGjmhhBCCCEBhYIbQgghhAQUCm4IIYQQElAouCGEEEJIQKHghhBCCCEBhYIbQgghhAQUCm4IIYQQElAouCGEEEJIQKHghhBCCCEBhYIbQgghhAQUCm4IIYQQElD+H+KLTaeUIWbaAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Test for homoscedasticity\n",
+    "homoscedasticity_test(model1_ols)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Results of indicate evidence against homoscedasticity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Using Log-transformed Price**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section, we explore the use of a multiple linear regression model with log-transformed price values to deal with the skewed data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use a multiple linear regression model of the data using all features\n",
+    "# Split data into train and test\n",
+    "X_train, X_test, y_train, y_test = train_test(output_log, 'log_price')\n",
+    "\n",
+    "#create linear regression model for price and setting up cross validation \n",
+    "model_log = LinearRegression()\n",
+    "model_log.fit(X_train, y_train);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "actual_vs_predicted(model_log, X_test, y_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here - remember to use markdown cells for comments as well!"
+    "#add constant to X_train\n",
+    "X_train = sm.add_constant(X_train)\n",
+    "\n",
+    "#find OLS for train data set\n",
+    "model1_log_ols = sm.OLS(y_train, X_train).fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>        <td>log_price</td>    <th>  R-squared:         </th> <td>   0.772</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.771</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2853.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Mon, 29 Apr 2024</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>11:03:45</td>     <th>  Log-Likelihood:    </th> <td> -688.31</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td> 16914</td>      <th>  AIC:               </th> <td>   1419.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td> 16893</td>      <th>  BIC:               </th> <td>   1581.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>    20</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>         <td>   13.0481</td> <td>    0.002</td> <td> 6729.230</td> <td> 0.000</td> <td>   13.044</td> <td>   13.052</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>bedrooms</th>      <td>   -0.0315</td> <td>    0.003</td> <td>  -11.793</td> <td> 0.000</td> <td>   -0.037</td> <td>   -0.026</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>bathrooms</th>     <td>    0.0379</td> <td>    0.004</td> <td>   10.550</td> <td> 0.000</td> <td>    0.031</td> <td>    0.045</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_living</th>   <td>    0.0654</td> <td>    0.011</td> <td>    5.740</td> <td> 0.000</td> <td>    0.043</td> <td>    0.088</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_lot</th>      <td>    0.0166</td> <td>    0.005</td> <td>    3.252</td> <td> 0.001</td> <td>    0.007</td> <td>    0.027</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>floors</th>        <td>    0.0295</td> <td>    0.003</td> <td>    9.723</td> <td> 0.000</td> <td>    0.024</td> <td>    0.035</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>waterfront</th>    <td>    0.0434</td> <td>    0.002</td> <td>   21.791</td> <td> 0.000</td> <td>    0.039</td> <td>    0.047</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>view</th>          <td>    0.0362</td> <td>    0.002</td> <td>   17.247</td> <td> 0.000</td> <td>    0.032</td> <td>    0.040</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>condition</th>     <td>   -0.0407</td> <td>    0.002</td> <td>  -18.925</td> <td> 0.000</td> <td>   -0.045</td> <td>   -0.037</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>grade</th>         <td>    0.1995</td> <td>    0.003</td> <td>   57.321</td> <td> 0.000</td> <td>    0.193</td> <td>    0.206</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_above</th>    <td>    0.0862</td> <td>    0.010</td> <td>    8.409</td> <td> 0.000</td> <td>    0.066</td> <td>    0.106</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_basement</th> <td>    0.0482</td> <td>    0.005</td> <td>    8.775</td> <td> 0.000</td> <td>    0.037</td> <td>    0.059</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>yr_built</th>      <td>   -0.1087</td> <td>    0.003</td> <td>  -36.010</td> <td> 0.000</td> <td>   -0.115</td> <td>   -0.103</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>yr_renovated</th>  <td>    0.0145</td> <td>    0.002</td> <td>    7.092</td> <td> 0.000</td> <td>    0.011</td> <td>    0.019</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>zipcode</th>       <td>   -0.0292</td> <td>    0.003</td> <td>  -11.649</td> <td> 0.000</td> <td>   -0.034</td> <td>   -0.024</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>lat</th>           <td>    0.1887</td> <td>    0.002</td> <td>   89.183</td> <td> 0.000</td> <td>    0.185</td> <td>    0.193</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>long</th>          <td>   -0.0157</td> <td>    0.003</td> <td>   -5.917</td> <td> 0.000</td> <td>   -0.021</td> <td>   -0.010</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_living15</th> <td>    0.0712</td> <td>    0.003</td> <td>   21.749</td> <td> 0.000</td> <td>    0.065</td> <td>    0.078</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_lot15</th>    <td>   -0.0366</td> <td>    0.005</td> <td>   -7.325</td> <td> 0.000</td> <td>   -0.046</td> <td>   -0.027</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>month_of_date</th> <td>    0.0068</td> <td>    0.003</td> <td>    2.175</td> <td> 0.030</td> <td>    0.001</td> <td>    0.013</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>year_of_date</th>  <td>    0.0297</td> <td>    0.003</td> <td>    9.511</td> <td> 0.000</td> <td>    0.024</td> <td>    0.036</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>337.762</td> <th>  Durbin-Watson:     </th> <td>   1.995</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 678.394</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 0.098</td>  <th>  Prob(JB):          </th> <td>4.88e-148</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 3.961</td>  <th>  Cond. No.          </th> <td>    18.9</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\textbf{Dep. Variable:}    &    log\\_price    & \\textbf{  R-squared:         } &     0.772   \\\\\n",
+       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.771   \\\\\n",
+       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     2853.   \\\\\n",
+       "\\textbf{Date:}             & Mon, 29 Apr 2024 & \\textbf{  Prob (F-statistic):} &     0.00    \\\\\n",
+       "\\textbf{Time:}             &     11:03:45     & \\textbf{  Log-Likelihood:    } &   -688.31   \\\\\n",
+       "\\textbf{No. Observations:} &       16914      & \\textbf{  AIC:               } &     1419.   \\\\\n",
+       "\\textbf{Df Residuals:}     &       16893      & \\textbf{  BIC:               } &     1581.   \\\\\n",
+       "\\textbf{Df Model:}         &          20      & \\textbf{                     } &             \\\\\n",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                         & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{const}           &      13.0481  &        0.002     &  6729.230  &         0.000        &       13.044    &       13.052     \\\\\n",
+       "\\textbf{bedrooms}        &      -0.0315  &        0.003     &   -11.793  &         0.000        &       -0.037    &       -0.026     \\\\\n",
+       "\\textbf{bathrooms}       &       0.0379  &        0.004     &    10.550  &         0.000        &        0.031    &        0.045     \\\\\n",
+       "\\textbf{sqft\\_living}    &       0.0654  &        0.011     &     5.740  &         0.000        &        0.043    &        0.088     \\\\\n",
+       "\\textbf{sqft\\_lot}       &       0.0166  &        0.005     &     3.252  &         0.001        &        0.007    &        0.027     \\\\\n",
+       "\\textbf{floors}          &       0.0295  &        0.003     &     9.723  &         0.000        &        0.024    &        0.035     \\\\\n",
+       "\\textbf{waterfront}      &       0.0434  &        0.002     &    21.791  &         0.000        &        0.039    &        0.047     \\\\\n",
+       "\\textbf{view}            &       0.0362  &        0.002     &    17.247  &         0.000        &        0.032    &        0.040     \\\\\n",
+       "\\textbf{condition}       &      -0.0407  &        0.002     &   -18.925  &         0.000        &       -0.045    &       -0.037     \\\\\n",
+       "\\textbf{grade}           &       0.1995  &        0.003     &    57.321  &         0.000        &        0.193    &        0.206     \\\\\n",
+       "\\textbf{sqft\\_above}     &       0.0862  &        0.010     &     8.409  &         0.000        &        0.066    &        0.106     \\\\\n",
+       "\\textbf{sqft\\_basement}  &       0.0482  &        0.005     &     8.775  &         0.000        &        0.037    &        0.059     \\\\\n",
+       "\\textbf{yr\\_built}       &      -0.1087  &        0.003     &   -36.010  &         0.000        &       -0.115    &       -0.103     \\\\\n",
+       "\\textbf{yr\\_renovated}   &       0.0145  &        0.002     &     7.092  &         0.000        &        0.011    &        0.019     \\\\\n",
+       "\\textbf{zipcode}         &      -0.0292  &        0.003     &   -11.649  &         0.000        &       -0.034    &       -0.024     \\\\\n",
+       "\\textbf{lat}             &       0.1887  &        0.002     &    89.183  &         0.000        &        0.185    &        0.193     \\\\\n",
+       "\\textbf{long}            &      -0.0157  &        0.003     &    -5.917  &         0.000        &       -0.021    &       -0.010     \\\\\n",
+       "\\textbf{sqft\\_living15}  &       0.0712  &        0.003     &    21.749  &         0.000        &        0.065    &        0.078     \\\\\n",
+       "\\textbf{sqft\\_lot15}     &      -0.0366  &        0.005     &    -7.325  &         0.000        &       -0.046    &       -0.027     \\\\\n",
+       "\\textbf{month\\_of\\_date} &       0.0068  &        0.003     &     2.175  &         0.030        &        0.001    &        0.013     \\\\\n",
+       "\\textbf{year\\_of\\_date}  &       0.0297  &        0.003     &     9.511  &         0.000        &        0.024    &        0.036     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 337.762 & \\textbf{  Durbin-Watson:     } &     1.995  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &   678.394  \\\\\n",
+       "\\textbf{Skew:}          &   0.098 & \\textbf{  Prob(JB):          } & 4.88e-148  \\\\\n",
+       "\\textbf{Kurtosis:}      &   3.961 & \\textbf{  Cond. No.          } &      18.9  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:              log_price   R-squared:                       0.772\n",
+       "Model:                            OLS   Adj. R-squared:                  0.771\n",
+       "Method:                 Least Squares   F-statistic:                     2853.\n",
+       "Date:                Mon, 29 Apr 2024   Prob (F-statistic):               0.00\n",
+       "Time:                        11:03:45   Log-Likelihood:                -688.31\n",
+       "No. Observations:               16914   AIC:                             1419.\n",
+       "Df Residuals:                   16893   BIC:                             1581.\n",
+       "Df Model:                          20                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "=================================================================================\n",
+       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "---------------------------------------------------------------------------------\n",
+       "const            13.0481      0.002   6729.230      0.000      13.044      13.052\n",
+       "bedrooms         -0.0315      0.003    -11.793      0.000      -0.037      -0.026\n",
+       "bathrooms         0.0379      0.004     10.550      0.000       0.031       0.045\n",
+       "sqft_living       0.0654      0.011      5.740      0.000       0.043       0.088\n",
+       "sqft_lot          0.0166      0.005      3.252      0.001       0.007       0.027\n",
+       "floors            0.0295      0.003      9.723      0.000       0.024       0.035\n",
+       "waterfront        0.0434      0.002     21.791      0.000       0.039       0.047\n",
+       "view              0.0362      0.002     17.247      0.000       0.032       0.040\n",
+       "condition        -0.0407      0.002    -18.925      0.000      -0.045      -0.037\n",
+       "grade             0.1995      0.003     57.321      0.000       0.193       0.206\n",
+       "sqft_above        0.0862      0.010      8.409      0.000       0.066       0.106\n",
+       "sqft_basement     0.0482      0.005      8.775      0.000       0.037       0.059\n",
+       "yr_built         -0.1087      0.003    -36.010      0.000      -0.115      -0.103\n",
+       "yr_renovated      0.0145      0.002      7.092      0.000       0.011       0.019\n",
+       "zipcode          -0.0292      0.003    -11.649      0.000      -0.034      -0.024\n",
+       "lat               0.1887      0.002     89.183      0.000       0.185       0.193\n",
+       "long             -0.0157      0.003     -5.917      0.000      -0.021      -0.010\n",
+       "sqft_living15     0.0712      0.003     21.749      0.000       0.065       0.078\n",
+       "sqft_lot15       -0.0366      0.005     -7.325      0.000      -0.046      -0.027\n",
+       "month_of_date     0.0068      0.003      2.175      0.030       0.001       0.013\n",
+       "year_of_date      0.0297      0.003      9.511      0.000       0.024       0.036\n",
+       "==============================================================================\n",
+       "Omnibus:                      337.762   Durbin-Watson:                   1.995\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              678.394\n",
+       "Skew:                           0.098   Prob(JB):                    4.88e-148\n",
+       "Kurtosis:                       3.961   Cond. No.                         18.9\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model1_log_ols.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The R-squared of 0.772 indicates that approximately 77.2% of the variability in the log-transformed price is explained by the independent variables in the model. This also translates to our best predictive model.A high F-statistic value (2853.0 in this case) with a low p-value (0.00) indicates that the overall model is statistically significant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Normality test**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, we assess the normality of residuals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Jarque-Bera ----\n",
+      "678.3942925790197\n",
+      "\n",
+      " Prob ----\n",
+      "4.8814752212043025e-148\n",
+      "\n",
+      " Skew ----\n",
+      "0.09839931206621617\n",
+      "\n",
+      " Kurtosis ----\n",
+      "3.9611831552562005\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Test for normality\n",
+    "normality_test(model1_log_ols);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Test for Homoscedasticity**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Breusch-Pagan test ----\n",
+      "                                       value\n",
+      "Lagrange multiplier statistic   8.409239e+02\n",
+      "p-value                        2.876379e-165\n",
+      "f-value                         4.419107e+01\n",
+      "f p-value                      9.834783e-170\n",
+      "\n",
+      " Goldfeld-Quandt test ----\n",
+      "                value\n",
+      "F statistic  0.965667\n",
+      "p-value      0.945676\n",
+      "\n",
+      " Residuals plot ----\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Test for homoscedasticity\n",
+    "homoscedasticity_test(model1_log_ols)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Results Interpretation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Model1_log, our top-performing multiple linear regression model that makes use of all features. More precisely, all positive variables are transformed by this multi-linear regression model by utilizing the power of log.\n",
+    "\n",
+    "- The simple multiple linear regression model reveals that amongst all features, location features and square footage of living space seems to have the biggest impact on housing prices.\n",
+    "\n",
+    "- In terms of handling outlier values, the multiple linear regression model is the most reliable. It also shows to be the most accurate when compared to its R-Squared and RMSE values. \n",
+    "\n",
+    "- The model still has certain assumption restrictions, such as a high conditional number (multicollinearity)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Recommendations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- We would have to either partition the dataset into groups based on house size or add more data to the model in order to improve its accuracy and produce various models for different types of homes.\n",
+    "\n",
+    "- Outliers within the dataset can also contribute to discrepencies within the model. Further investigation into outliers within the dataset could also help further improve the model.\n",
+    "\n",
+    "- The model can be further improved with additional datasets within the same county (King County), or from different counties nearby KIng County.\n",
+    "\n",
+    "- At the moment, the model is tuned for a dataset that includes both larger houses and small-to-medium-sized houses. A division and differentiation between the two house styles might also aid in the model's improvement."
    ]
   }
  ],
@@ -40,7 +7003,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,