diff --git a/student.ipynb b/student.ipynb
index d3bb34af..df431227 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -7,21 +7,1763 @@
     "## Final Project Submission\n",
     "\n",
     "Please fill out:\n",
-    "* Student name: \n",
-    "* Student pace: self paced / part time / full time\n",
+    "* Student names: CALVIN OMWEGA,COLLINS BIWOTT,INGAVI KILAVUKA,MERCY KIRAGU\n",
+    "                 \n",
+    "* Student pace: Full time-Hybrid\n",
     "* Scheduled project review date/time: \n",
-    "* Instructor name: \n",
-    "* Blog post URL:\n"
+    "* Instructor name: Maryann Mwikali\n",
+    "* Blog post URL: \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BUSINESS UNDERSTANDING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BUSINESS OVERVIEW"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A real estate agency aims to provide detailed pricing models to their clients showing how different features impact home sale prices.By understanding these impacts,the agency can advise sellers on how to enhance their property to maximize sale price and assist buyers in evaluating potential homes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CHALLENGES:\n",
+    "\n",
+    "1.The King County real estate market is subject to fluctuations influenced by economic conditions, interest rates, and other external factors, posing challenges in predicting property values accurately.\n",
+    "\n",
+    "2.The presence of numerous real estate agencies and agents vying for clients' attention intensifies competition, requiring innovative strategies to stand out and attract business.\n",
+    "\n",
+    "3.Ensuring the accuracy and completeness of data sources, as well as accessing relevant datasets for analysis, presents challenges in developing robust pricing models and predictive analytics.\n",
+    "\n",
+    "4.Ongoing regulatory changes, such as zoning regulations, tax policies, and housing laws, can impact market dynamics and require adaptation to ensure compliance and mitigate risks.\n",
+    "\n",
+    "#### PROPOSED SOLUTION:\n",
+    "\n",
+    "1.Implement sophisticated data analytics techniques, including machine learning algorithms, to analyze historical sales data, identify trends, and predict future property values with greater accuracy.\n",
+    "\n",
+    "2.Develop customized pricing models that consider a wide range of factors, including property features, neighborhood characteristics, market demand, and buyer preferences, to provide tailored pricing recommendations for each property.\n",
+    "\n",
+    "3.Establish a framework for continuous monitoring of market trends and model performance, allowing for timely adjustments to pricing strategies and recommendations based on changing market conditions.\n",
+    "\n",
+    "4.Foster collaborations with data providers, industry experts, and technology partners to access additional data sources, enhance analytical capabilities, and stay abreast of best practices in real estate valuation and predictive modeling.\n",
+    "\n",
+    "In conclusion, by leveraging advanced analytics and innovative strategies, the real estate agency can overcome challenges in the dynamic King County market and provide clients with valuable insights and recommendations to optimize their real estate transactions. Through continuous adaptation, collaboration, and a client-centric approach, the agency can achieve sustainable growth, enhance competitiveness, and deliver exceptional value to clients in the ever-evolving real estate landscape."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PROBLEM STATEMENT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "The real estate agency seeks to develop sophisticated pricing models that analyze the King County housing market data to determine how different features influence home sale prices. By understanding these impacts, the agency aims to:\n",
+    "1. Assist sellers in optimizing their property to maximize sale price: Sellers will benefit from tailored recommendations on which features to enhance or highlight to increase the value of their homes. By leveraging insights from the pricing models, sellers can make informed decisions about renovations, upgrades, or staging strategies to attract potential buyers and achieve optimal sale prices.\n",
+    "2. Empower buyers to make informed purchasing decisions: Buyers will gain valuable insights into how various property features correlate with sale prices. Armed with this knowledge, buyers can prioritize their preferences and make informed decisions when evaluating potential homes. Additionally, the agency can provide guidance on negotiating strategies based on the perceived value of different features.\n",
+    "3. Enhance the agency's competitive advantage: By offering advanced pricing models that provide granular insights into the factors influencing home sale prices, the agency can differentiate itself in the market. This will attract both sellers seeking to maximize their returns and buyers seeking expert guidance in their property search process.\n",
+    "\n",
+    "Overall, the development of detailed pricing models will enable the real estate agency to provide superior value to its clients, facilitate more informed decision-making processes, and maintain a competitive edge in the dynamic King County housing market."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DATA UNDERSTANDING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The King County House sale dataset contains information regarding houses sold during the one year period ranging from May 2014 to May 2015.\n",
+    "\n",
+    "In order to understand what each column in our data frame represents, a data dictionary is displayed below:\n",
+    "\n",
+    "##### TARGET/DEPENDENT VARIABLE:\n",
+    "\n",
+    "price — price of each home sold\n",
+    "\n",
+    "##### PREDICTORS/INDEPENDENT VARIABLES:\n",
+    "\n",
+    "id — unique identifier for a house\n",
+    "\n",
+    "date — date of the home sale\n",
+    "\n",
+    "bedrooms — number of bedrooms\n",
+    "\n",
+    "bathrooms — number of bathrooms\n",
+    "\n",
+    "sqft_living — square footage of the house’s interior living space\n",
+    "\n",
+    "sqft_lot — square footage of the land space\n",
+    "\n",
+    "floors — number of floors\n",
+    "\n",
+    "waterfront — does the house have a view to the waterfront?\n",
+    "\n",
+    "view — an index from 0 to 4 of how good the view of the property was\n",
+    "\n",
+    "condition — an index from 1 to 5 on the condition of the house\n",
+    "grade — an index from 1 to 13, where 1–3 falls short of building construction and design, 7 has an average level of construction and design, and 11–13 have a high quality level of construction and design\n",
+    "\n",
+    "sqft_above — square feet above ground\n",
+    "\n",
+    "sqft_basement — square feet below ground\n",
+    "\n",
+    "yr_built— the year the house was initially built\n",
+    "\n",
+    "yr_renovated — the year of the house’s last renovation (0 if never renovated)\n",
+    "\n",
+    "zipcode — zip\n",
+    "\n",
+    "lat — latitude coordinate\n",
+    "\n",
+    "long — longitude coordinate\n",
+    "\n",
+    "sqft_living15 — average size of interior housing living space for the closest 15 houses, in square feet\n",
+    "\n",
+    "sqft_lot15 — average size of land lot for the closest 15 houses, in square feet\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### DATA PREPROCESSING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This dataset contains information on house sales in King County, WA between May 2014 and May 2015 and is found in this project's repository. The data cleaning on this dataset will be extremely helpful when creating a regression model for predicting house prices:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### IMPORTING NECESSARY LIBRARIES"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### LOADING THE DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "dataset = pd.read_csv(\"data/kc_house_data.csv\",index_col = 0 )\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "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": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#checking the first five rows\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "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": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 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": [
+    "dataset.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The columns in the dataset are integers,floats and objects representing categorical variables.Some columns contain missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "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>Text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td># Column Names and Descriptions for King Count...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>* `id` - Unique identifier for a house\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>* `date` - Date house was sold\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>* `price` - Sale price (prediction target)\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>* `bedrooms` - Number of bedrooms\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>* `bathrooms` - Number of bathrooms\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>* `sqft_living` - Square footage of living spa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>* `sqft_lot` - Square footage of the lot\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>* `floors` - Number of floors (levels) in house\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>* `waterfront` - Whether the house is on a wat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>* Includes Duwamish, Elliott Bay, Puget Soun...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>* `view` - Quality of view from house\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>* Includes views of Mt. Rainier, Olympics, C...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>* `condition` - How good the overall condition...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>* See the [King County Assessor Website](htt...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>* `grade` - Overall grade of the house. Relate...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>* See the [King County Assessor Website](htt...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>* `sqft_above` - Square footage of house apart...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>* `sqft_basement` - Square footage of the base...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>* `yr_built` - Year when house was built\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>* `yr_renovated` - Year when house was renovat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>* `zipcode` - ZIP Code used by the United Stat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>* `lat` - Latitude coordinate\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>* `long` - Longitude coordinate\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>* `sqft_living15` - The square footage of inte...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>* `sqft_lot15` - The square footage of the lan...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 Text\n",
+       "0   # Column Names and Descriptions for King Count...\n",
+       "1            * `id` - Unique identifier for a house\\n\n",
+       "2                    * `date` - Date house was sold\\n\n",
+       "3        * `price` - Sale price (prediction target)\\n\n",
+       "4                 * `bedrooms` - Number of bedrooms\\n\n",
+       "5               * `bathrooms` - Number of bathrooms\\n\n",
+       "6   * `sqft_living` - Square footage of living spa...\n",
+       "7          * `sqft_lot` - Square footage of the lot\\n\n",
+       "8   * `floors` - Number of floors (levels) in house\\n\n",
+       "9   * `waterfront` - Whether the house is on a wat...\n",
+       "10    * Includes Duwamish, Elliott Bay, Puget Soun...\n",
+       "11            * `view` - Quality of view from house\\n\n",
+       "12    * Includes views of Mt. Rainier, Olympics, C...\n",
+       "13  * `condition` - How good the overall condition...\n",
+       "14    * See the [King County Assessor Website](htt...\n",
+       "15  * `grade` - Overall grade of the house. Relate...\n",
+       "16    * See the [King County Assessor Website](htt...\n",
+       "17  * `sqft_above` - Square footage of house apart...\n",
+       "18  * `sqft_basement` - Square footage of the base...\n",
+       "19         * `yr_built` - Year when house was built\\n\n",
+       "20  * `yr_renovated` - Year when house was renovat...\n",
+       "21  * `zipcode` - ZIP Code used by the United Stat...\n",
+       "22                    * `lat` - Latitude coordinate\\n\n",
+       "23                  * `long` - Longitude coordinate\\n\n",
+       "24  * `sqft_living15` - The square footage of inte...\n",
+       "25  * `sqft_lot15` - The square footage of the lan..."
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Reading the column_names.md Dataset\n",
+    "\n",
+    "with open('data/column_names.md', 'r') as file:\n",
+    "    md_lines = file.readlines()\n",
+    "\n",
+    "dataset_col = pd.DataFrame({'Text': md_lines})\n",
+    "\n",
+    "dataset_col"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(21597, 20)"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data has 21597 entries and 20 columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "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": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#checking the columns in the dataset\n",
+    "dataset.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "int64      8\n",
+      "float64    6\n",
+      "object     6\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#CHECKING THE DATATYPES OF THE FEATURES ABOVE\n",
+    "column_data_types_counts = dataset.dtypes.value_counts()\n",
+    "\n",
+    "print(column_data_types_counts)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### DATA CLEANING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data Cleaning is the First Step Towards Predictive Accuracy,In order to produce a trustworthy and accurate model,we will perform data cleaning beforehand!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### CHECKING FOR MISSING VALUES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Missing values are often times problematic and should always be checked when reviewing a dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date             False\n",
+       "price            False\n",
+       "bedrooms         False\n",
+       "bathrooms        False\n",
+       "sqft_living      False\n",
+       "sqft_lot         False\n",
+       "floors           False\n",
+       "waterfront        True\n",
+       "view              True\n",
+       "condition        False\n",
+       "grade            False\n",
+       "sqft_above       False\n",
+       "sqft_basement    False\n",
+       "yr_built         False\n",
+       "yr_renovated      True\n",
+       "zipcode          False\n",
+       "lat              False\n",
+       "long             False\n",
+       "sqft_living15    False\n",
+       "sqft_lot15       False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking if there are any missing values in the features\n",
+    "dataset.isnull().any()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date              0.000000\n",
+       "price             0.000000\n",
+       "bedrooms          0.000000\n",
+       "bathrooms         0.000000\n",
+       "sqft_living       0.000000\n",
+       "sqft_lot          0.000000\n",
+       "floors            0.000000\n",
+       "waterfront       11.001528\n",
+       "view              0.291707\n",
+       "condition         0.000000\n",
+       "grade             0.000000\n",
+       "sqft_above        0.000000\n",
+       "sqft_basement     0.000000\n",
+       "yr_built          0.000000\n",
+       "yr_renovated     17.789508\n",
+       "zipcode           0.000000\n",
+       "lat               0.000000\n",
+       "long              0.000000\n",
+       "sqft_living15     0.000000\n",
+       "sqft_lot15        0.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#checking for the percentage of missing values per column\n",
+    "dataset.isnull().sum() / len(dataset) * 100\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Waterfront, view, and yr_renovated have missing values. If we decide to use these variables in the predictive model it will be problematic hence we'll fix the missing values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### FIXING MISSING VALUES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned above, waterfront, view, and yr_renovated are the only columns with missing values.We'll fix the missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "waterfront\n",
+      "NO     0.992404\n",
+      "YES    0.007596\n",
+      "Name: waterfront, dtype: float64\n",
+      "------------------\n",
+      "view\n",
+      "NONE         0.901923\n",
+      "AVERAGE      0.044441\n",
+      "GOOD         0.023591\n",
+      "FAIR         0.015325\n",
+      "EXCELLENT    0.014721\n",
+      "Name: view, dtype: float64\n",
+      "------------------\n",
+      "yr_renovated\n",
+      "0.0       0.958096\n",
+      "2014.0    0.004112\n",
+      "2003.0    0.001746\n",
+      "2013.0    0.001746\n",
+      "2007.0    0.001690\n",
+      "Name: yr_renovated, dtype: float64\n",
+      "------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "#checking the percentage\n",
+    "for col in [\"waterfront\", \"view\", \"yr_renovated\"]:\n",
+    "    print(col)\n",
+    "    print(dataset[col].value_counts(normalize = True).sort_values(ascending = False).head())\n",
+    "    print(\"------------------\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results shown above clearly show that 0 is the most common value for all three columns!,where NONE in the views column is equivalent to 0.In order to retain as much data as possible, we’ll fill the missing values of each column by randomly choosing a unique value given the appropriate weights. For example, a missing value in the waterfront column will be replaced by 0 with a 99% chance and 1 with a 1% chance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The number of missing values in waterfront is: 0\n",
+      "The number of missing values in view is: 0\n",
+      "The number of missing values in yr_renovated is: 0\n",
+      "--------------------------------------\n",
+      "Missing values per column:\n",
+      "date             0\n",
+      "price            0\n",
+      "bedrooms         0\n",
+      "bathrooms        0\n",
+      "sqft_living      0\n",
+      "sqft_lot         0\n",
+      "floors           0\n",
+      "waterfront       0\n",
+      "view             0\n",
+      "condition        0\n",
+      "grade            0\n",
+      "sqft_above       0\n",
+      "sqft_basement    0\n",
+      "yr_built         0\n",
+      "yr_renovated     0\n",
+      "zipcode          0\n",
+      "lat              0\n",
+      "long             0\n",
+      "sqft_living15    0\n",
+      "sqft_lot15       0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "def replace_missing(val, probs):\n",
+    "    if pd.isnull(val):\n",
+    "        return np.random.choice(probs.index, p=probs)\n",
+    "    else:\n",
+    "        return val\n",
+    "\n",
+    "for col in [\"waterfront\", \"view\", \"yr_renovated\"]:\n",
+    "    # Check if there are missing values in the column\n",
+    "    if dataset[col].isnull().sum() > 0:\n",
+    "        # Calculate the probabilities of each unique value\n",
+    "        unique_p = dataset[col].value_counts(normalize=True)\n",
+    "        # Apply the function to replace missing values\n",
+    "        dataset[col] = dataset[col].apply(replace_missing, args=(unique_p,))\n",
+    "        print(\"The number of missing values in {} is:\".format(col), dataset[col].isnull().sum())\n",
+    "\n",
+    "print(\"--------------------------------------\")\n",
+    "print(\"Missing values per column:\")\n",
+    "# Last check to see if there are missing values\n",
+    "print(dataset.isnull().sum())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No more missing values and we retained all data we started off with."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CHECKING FOR DUPLICATED VALUES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Duplicates are also problematic and are far more subtle than missing values. Given that a single house could’ve been sold at different points in time, we’ll define a duplicate as any row that has the same `id` and `date` as any other row in the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if the number of duplicates is 0\n",
+    "dataset.duplicated().sum() == 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are no duplicated values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### DROPPING THE COLUMNS THAT WE WON'T BE USING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here - remember to use markdown cells for comments as well!"
+    "dataset = dataset.drop(columns=['sqft_basement', 'zipcode', 'lat', 'long', 'sqft_living15'\n",
+    "                     ,'sqft_lot15','sqft_above'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "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>yr_built</th>\n",
+       "      <th>yr_renovated</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",
+       "    </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>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</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>1951</td>\n",
+       "      <td>1991.0</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>1933</td>\n",
+       "      <td>0.0</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>1965</td>\n",
+       "      <td>0.0</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>1987</td>\n",
+       "      <td>0.0</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  yr_built  \\\n",
+       "id                                                                        \n",
+       "7129300520     1.0         NO  NONE    Average      7 Average      1955   \n",
+       "6414100192     2.0         NO  NONE    Average      7 Average      1951   \n",
+       "5631500400     1.0         NO  NONE    Average  6 Low Average      1933   \n",
+       "2487200875     1.0         NO  NONE  Very Good      7 Average      1965   \n",
+       "1954400510     1.0         NO  NONE    Average         8 Good      1987   \n",
+       "\n",
+       "            yr_renovated  \n",
+       "id                        \n",
+       "7129300520           0.0  \n",
+       "6414100192        1991.0  \n",
+       "5631500400           0.0  \n",
+       "2487200875           0.0  \n",
+       "1954400510           0.0  "
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#the features that we will be working with\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## EDA \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualizing the distribution of 'price'\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.histplot(dataset['price'], kde=True, color='blue')\n",
+    "plt.title('Distribution of House Prices')\n",
+    "plt.xlabel('Price')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This histogram represents the frequency of houses at different price points. The plot also includes a Kernel Density Estimate (KDE), which is the smooth curve showing the distribution shape. \n",
+    "\n",
+    "Central Tendency: The peak of the KDE suggests where the bulk of house prices are concentrated. A peak in a lower price range might suggest affordability is more common.\n",
+    "\n",
+    "Spread: The width of the distribution indicates variability in house prices. A wider spread means prices are highly variable, impacting buyer's options.\n",
+    "\n",
+    "Skewness: If the tail of the histogram extends further to the right, it indicates a right-skewed distribution, meaning there are a few very expensive houses.\n",
+    "\n",
+    "Outliers: Extreme values on the higher end can be seen as separate, sparse bars, indicating potential outliers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Relation between Price and Bedrooms/Bathrooms\n",
+    "plt.figure(figsize=(14, 7))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.boxplot(x='bedrooms', y='price', data=dataset)\n",
+    "plt.title('Price Distribution by Number of Bedrooms')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.boxplot(x='bathrooms', y='price', data=dataset)\n",
+    "plt.title('Price Distribution by Number of Bathrooms')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " These boxplots show how house prices vary with the number of bedrooms and bathrooms.\n",
+    "Median Price: The line in the middle of each box indicates the median price for each category, providing a sense of the typical price.\n",
+    "\n",
+    "Price Range: The height of the boxes shows the interquartile range (middle 50% of data), which helps understand the typical price variation within each category.\n",
+    "\n",
+    "Outliers: Points above or below the whiskers represent outliers, which are significantly higher or lower priced houses than typical for that number of bedrooms or bathrooms.\n",
+    "\n",
+    "Trends: Generally, more bedrooms and bathrooms correlate with higher prices, but diminishing returns or price saturation can be observed in higher categories."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Year Built and Renovated Impact\n",
+    "plt.figure(figsize=(14, 7))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.scatterplot(x='yr_built', y='price', data=dataset, alpha=0.5)\n",
+    "plt.title('Price vs. Year Built')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.scatterplot(x='yr_renovated', y='price', data=dataset, alpha=0.5)\n",
+    "plt.title('Price vs. Year Renovated')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Scatter plots showing how prices compare with the year the house was built and the year it was renovated.\n",
+    "\n",
+    "Trends Over Time: Older houses might not necessarily be cheaper; historical or well-maintained homes can also command high prices.\n",
+    "\n",
+    "Renovation Impact: Renovated homes can vary widely in price, suggesting that the quality and extent of renovations, along with the original house's characteristics, significantly impact value.\n",
+    "\n",
+    "Age and Price: Newer homes tend to have a more consistent price range, often at a premium due to modern features and less anticipated maintenance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Statistical Summaries\n",
+    "\n",
+    "A summary table provides a detailed statistical overview of numerical features, including mean, median, standard deviation, and quartiles, which are critical for understanding the central tendencies and spread of data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Boxplots for Outlier Detection\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.boxplot(data=dataset[['price', 'sqft_living', 'sqft_lot']], palette='Set2')\n",
+    "plt.title('Boxplot of Price, Sqft Living, and Sqft Lot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Boxplots for price, square footage of the living area, and lot size to detect outliers.\n",
+    "\n",
+    "Price and Size Relationship: Understanding how the size of the house and the lot correlates with price and identifying extreme values in each category.\n",
+    "\n",
+    "Outlier Detection: Significant outliers can skew analysis and model predictions, so they might need to be handled (e.g., by capping, transformation, or removal)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x1080 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Analysis of Categorical Variables\n",
+    "categorical_vars = ['waterfront', 'view', 'condition', 'grade']\n",
+    "plt.figure(figsize=(20, 15))\n",
+    "for i, var in enumerate(categorical_vars, 1):\n",
+    "    plt.subplot(2, 2, i)\n",
+    "    sns.boxplot(x=var, y='price', data=dataset)\n",
+    "    plt.title(f'Price Distribution by {var}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These variables can have a significant impact on property prices, and analyzing their distribution can provide key insights.\n",
+    "\n",
+    "Waterfront:\n",
+    "This box plot shows the distribution of house prices based on whether the house is on the waterfront or not.\n",
+    "\n",
+    "Key Observations:\n",
+    "Premium Prices for Waterfront: Houses on the waterfront (typically marked as 1) have significantly higher median prices compared to non-waterfront houses (0). This premium is due to the high desirability and limited availability of waterfront properties.\n",
+    "\n",
+    "Variability and Outliers: Waterfront properties tend to show greater variability in price and more outliers, indicating that some waterfront properties are extremely high-priced, likely due to superior views, larger sizes, or better amenities.\n",
+    "\n",
+    "View: \n",
+    "This variable typically rates the quality of the view from the house, often on a scale from 0 (no view) to higher numbers indicating better views.\n",
+    "\n",
+    "Increasing Price with Better Views: There is a clear trend where houses with better views (higher view ratings) command higher median prices. This demonstrates the value placed on aesthetic and environmental factors.\n",
+    "\n",
+    "Outliers: As the quality of the view increases, so does the range of prices and the number of outliers, suggesting that top-tier views are associated with luxury real estate.\n",
+    "\n",
+    "Condition: \n",
+    "This variable rates the overall condition of the house, from poor to excellent.\n",
+    "\n",
+    "Condition and Price Correlation: Surprisingly, the relationship between condition and price might not be as linear as expected. While better condition might imply higher prices, the highest prices aren't necessarily found in homes rated in the best condition. This might reflect the value of location or lot size over mere structural condition.\n",
+    "\n",
+    "Outliers in Good Condition: Homes in 'Good' or 'Very Good' condition may show a range of prices, with some outliers at the high end, perhaps due to renovations or other value-adding modifications.\n",
+    "\n",
+    "Grade: \n",
+    "Architectural grade, which reflects the construction quality and design of the building, ranging from low to high grades.\n",
+    "\n",
+    "Strong Correlation with Price: There is a very strong correlation between grade and price. Higher-grade homes, which indicate better construction and design quality, consistently show higher median prices.\n",
+    "\n",
+    "Price Spread and Grade: The spread and range of prices increase with higher grades, and the presence of outliers becomes more pronounced, indicating a significant premium for high-grade properties.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Correlation Analysis\n",
+    "\n",
+    "A heatmap of correlations between different features provides insights into relationships that can be leveraged or need to be adjusted for in predictive modeling.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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V6V378bvzriM8IqrO+oEsNtqQt7vyf5m/25KeVL2zGBvFfmUgNspQUGSxwAXHhWKBuas8zF3lEKz25GURHVf5vIiKS2HHlsU+rxvMWsOYxXpnFo0xlwDvAs+Ur0oD/neA8pcaY+YZY+Z97uT+lhh/M1NLWshaS63pIhu8J39qCd1Qs41DjxjD3U9M42/X38/7/326GQLzh5oHY/9/d58u6Xz0yC28cfe1nHHcSK5+8LmKbW6Xi//e/U8+fexWlq3bxNot23wdsM/YWo9F7Se3Get+ZVBqO+Iiwxpct6Woq3XZBTB7heXsY1ycNcpFVq7FCd4+AbW/Rmpv/ZplPzP72/c46fdXAeB4ysjYsIIR48/kmv+8S1h4JN988LxPo/Wl2lq9/9Gp9e2i/OfUj0t44sNSXv6ylOF93XRJCebXSC3PizpfFU1ZVwJZQy5D/wUYAeQDWGvXAMl1FbbWTrXWHmatPWyiK/43BflbFW3dTmSVsWoRHduzd1sWxTXWp1C8LcsfITaJhMRkdu3MrFjelZ1FfNukOsv37n8oWdszKMjPbYbomldy23gys3MrlrN25ZIUH1etTJuoiIrLzUcP7k+ZxyG3oPoAtJjoKIb27cGsxSt9HrOvJMdEsb1gT8VyVuEektpE1Fr2y5WVl6AbWjcYFBRBbGTlm1dMlHdd9TKW2KgqZSIryyzaYHnhS4fXvnUoKoFdQTpeESCubQo52ZVXVnJ3ZRKbUPN8sW3TKt585mYuvvoxomPiAYhPbE9c2xS69PReiRk0fAIZG5c3S9y+kLfbEhdd+T+PjTbk77H7lWG/MlBQXmbf82N3MSzf5JCWFLwjvKLiUtidV/m82JOXSVRsnW/1TVY3mBm3abaHvzTkGb3XWluyb8EYE0KtuazAk/XRdDqeewoA8cMHUZZfwN7tO8ibu4ToHl2I7JKGCQ0l9cxJZH4cvOMruvbsR9avW9iRuZWy0lJ+/vFLhgwbVa1M5q9bvFlVYOO6lZSVldImJq623QW1ft06sWX7DrZmZVNaVsaXsxcwaugh1crszM2vOBZL123CsQ5xbaLJyS+kYLe3g1RcUsLPy1bTpUPwnvD6t09gS04hW3N3U+px+GLlFkZ3T61RrmBvKfMzdnBMlW31rRsstu2ChBiIiwaXC/p1MqzZWv00tnqrZUAX70k5NRH2lno7AQBR3s8WxEZBnzTD8k1BcQqsVafuh7Bz+2ayszIoKytl4czPOGTomGplcnb+ygsPXsm5f7mb5NQuFetj49uRkNiezG0bAFi9dDYpHbsTrLbutCTGGRLagNsFA7u5WLm5etp45WaHIT3cAKQnGfaWeDuJoSEQVj6gKzQEeqS6yMwJ3pRzUscB5O/cRMGuDDxlJaxf/Cmd+o45eMXfWFcCW0PGLH5vjLkBiDTGjAcuAz7yTVgNM/jVB0gcPYywdgmM3fA9a257DBPqbdrmqW+S9dn3JB0/mmNWfoWnqIjFF98AgPV4WHrFbQz75DmM203GS9MoXL7Wn035TdzuEM655BoeuPVvOB4PI4+dTMdO3fn283cBGDPxNObN+oaZ336K2x1CWHg4f7767opLT08/cAMrl86nMD+Xqy46gVPOupRR40/xY4saL8Tt5przp/C3e57C4zhMHn0E3dM68O7XPwJw2rFH883PvzDt659wu12Eh4Zy11/PxxjDztw8/v306ziOg2Mt44cPYeShhxzkLwauEJeLa8cN4S/TZuA4lskDutK9XRzv/uKd9X/aYO+b/LdrtnJE5/ZEhoUctG6wsha+XOBw1mgXrvJb5+zMhyHlt8NZuM6y7lfo0cHy50kuSsvg458r3/injHARGQYeC1/MdygO4jukuN0hTLngBp6+6/9wHA/Dx/yODuk9+OmrtwAYMf5Mvpj2FLsL83jnhTvK67j5x13e22qdesENvPb4tZSVlZKYnM7v/3S739ryWzkWPppVxvnHhWKMYcEaD1m5lmG9vfmUn1c5rMpw6JXu4qrTwigts7z3QxkAbSLhnHGhALiMd3LM/h9AgonLHcKRk2/i8xcvxlqHXkNPJSGlJyvmvAlA3+FnsadgBx88cTqlewsxxsXSn15hypUfExbRpta6LZ1xBW8mub6MrecYPWOMC7gImIB3iMcXwHO2Hjv4JLR38L5ymljC4nn+DiFgDNj9k79DCAiuhT/6O4SA8Ujsrf4OIWAM6RW82amm9uNCHYt94uJC/R1CQPnnFP/ft2b+mBHN1scZ+u1PfmlvQzKLkcAL1tpnAYwx7vJ1ew5YS0RERKSF0n0Wq/sGb+dwn0jg66YNR0REREQCSUMyixHW2oq5f9baQmNMcN5US0RERKQJ6D6L1e02xhy6b8EYMxQI3u93EhEREZGDakhm8UrgHWPMvrsTdwDObPKIRERERIJEaxizWO/OorV2rjGmD9Ab72zoldbaIL5xhIiIiIgczEE7i8aYsdba6caYU/fb1NMYg7X2PR/FJiIiIiJ+Vp/M4mhgOnBSLdssoM6iiIiItEqt4abcB+0sWmv/XX5D7s+stW83Q0wiIiIiEiDqNWbRWusYY/4KqLMoIiIiUq41THBpSO70K2PM1caYdGNM230Pn0UmIiIiIn7XkFvnXIh3jOJl+63v1nThiIiIiASP1nBT7oZ0Fvvh7SgejbfT+APwtC+CEhEREZHA0JDO4stAPvBo+fLZ5evOaOqgRERERIJBaxiz2JDOYm9r7aAqy98aYxY1dUAiIiIiEjga0llcaIw5wlo7G8AYMxz4yTdhiYiIiAQ+3WexuuHAH40xm8uXOwErjDFLAGutHdjk0YmIiIiIXzWkszjRZ1GIiIiIBCGNWazCWrvJl4GIiIiISOBpSGZRRERERKpoDZnFlj8qU0REREQaTZlFERERkUZSZlFEREREWjV1FkVERESkTroMLSIiItJIreGm3C2/hSIiIiLSaMosioiIiDSSy60JLiIiIiLSiimzKCIiItJIreHWOc3SWUxYPK85/kxQyBl4mL9DCBjOnCf8HUJACOnZ398hBIzclSX+DiFgZOZF+juEgNGlk78jCBzLVhT6O4QA08bfAbQKyiyKiIiINJJmQ4uIiIhIq6bMooiIiEgjtYYxi8osioiIiEidlFkUERERaSRlFkVERESkVVNmUURERKSRNBtaRERERFo1ZRZFREREGkljFkVERESkVVNmUURERKSRNGZRRERERFo1dRZFREREpE66DC0iIiLSWEYTXERERESkFVNmUURERKSRdOscEREREWnVlFkUERERaSTdOkdEREREWjVlFkVEREQaSWMWRURERCRoGGMmGmNWGWPWGmOuq2V7nDHmI2PMImPMMmPMBQfbpzKLIiIiIo0USGMWjTFu4AlgPJABzDXGfGitXV6l2F+A5dbak4wxScAqY8zr1tqSuvYbOC0UERERkd9iGLDWWru+vPP3JnDyfmUsEGOMMUAbYBdQdqCdKrMoIiIi0kjNOWbRGHMpcGmVVVOttVOrLHcEtlRZzgCG77ebx4EPgW1ADHCmtdY50N9VZ1FEREQkCJR3DKceoEhtPVe73/JxwC/AWKA78JUx5gdrbX5dO1VnUURERKSRAmw2dAaQXmU5DW8GsaoLgP9Yay2w1hizAegD/FzXTjVmUURERKRlmAv0NMZ0NcaEAWfhveRc1WZgHIAxJgXoDaw/0E6VWRQRERFprACaDW2tLTPG/BX4AnADL1hrlxlj/lS+/WngduAlY8wSvJetr7XW7jzQftVZFBEREWkhrLWfAp/ut+7pKr9vAyY0ZJ8torO4ZMFM/vvc/TiOw6jxpzBpyvnVti+Y8x3v//dpjHHhdrs5+6J/0KvfYACef+xWFs37kdi4BO549O3mD74JDXz2LpJPOIaSrGxmDDmp1jL9HrqR5Imj8RQVs+ii68hf6L31UtKEkfR78EaM28WWF95h3X3PNmfoPjHrl2U88Mq7OI7DyWNGcN7J1V8b85ev5ur7nyE1ORGAMYcP5uIpJwBw+9Ov8uPCpSTExvDmfTc1e+xN7aela7j37c9xHIffHX0oF04cWW373FUb+PuTb5LaLh6AcUP68n8nHgPAq1/P4v0fF2AM9OyYwq3nnUx4aGgzt6Dp9Ep3cfKIUIyBn1d4+O6XmneMmDwilD6dXJSWwdvflrB1pyXEDX86OZwQlzeRsGS9h6/mHfBuEwFv3dIZfP32nTiOw+CjT+fIiZdW2569fR0fv3QDmVuWMfrkvzN8wkUV237++iUW/fgOGENSx16ceN7dhISGN3cTmsyGZTOY/u6dWMdhwIjTGT6h5rH4/LUbyNqyjKNP+juHH1t5LIr35PPF6zeR/etqwDDx3LtI7TakmVvQdPp0cnPqqHCMgdnLS/lmfmmNMqeOCqNv5xBKyyz//XovGTsc4tsYzhkfTmyUC8daZi0rY8aimnUl+AR9Z9HxeHj1mXu4+tYnaJuYwm3X/JHBw0bRMb1bRZl+A4cxZNhojDFs2biGJ++7jrufmAbA0WNPYtwJZ/LcIzf7qwlNJuPl99j45GsMfuGeWrcnTRxFdI8ufNd3AvHDB3HI47cwc8QZ4HLR/9GbmXP8BRRnZHL07HfJ/Hg6hSvWNXMLmo7Hcbj3xbd5/Ia/kZwYz3k33svIoQPoltahWrnBfXrw0D//XKP+pNFHcPpxo7nlyVeaK2Sf8TgOd7/xKU9f+QdSEmI55+5nGT2wN91Tk6uVG9KzE4/99Zxq6zJz8nlj+hzeu+UvRISFcs3Ut/l87lJOPio43wiNgd8dHcqzH5eQt9vyt1PDWb7JQ1ZO5WTBPp1ctIsz3PvGXjolG343MozH399LmQemfriXkjJvZ/Gyk8NZtdnD5qz9JxoGB8fx8OUbt3HWlS8Sm5DCS3efRs+BY2mX2qOiTERUPOPPupE1v3xTrW5BTibzpr/CJbd8SmhYBO9PvYLlcz9h4FGnNnczmoTjePj67ds4/W8vEhOfwmv3nkb3AWNp16HKsYiOZ+zpN7J20Tc16k9/90669hvJyZc8iqeshNKS4uYMv0kZA6cdE85T/ysit9By1ZmRLF1fRmaV10jfzm6S4l3c+eoeOqe4OP2YcB56pwjHgQ9+LCFjh0N4KPzjzChWba5etyXy3q6wZQucC+2NtH7NMpI7pJPcPo2Q0FCGHT2BhXO+r1YmIjKq4p+5t7io2j+2d/9DadMmtllj9pVdP86jdFdendtTJo9j62v/AyB3ziJC42IJb59E/LCB7Fm3iaINGdjSUra99QkpJ41rpqh9Y9najaS1T6JjSjtCQ0KYcORQZsxbXO/6h/btSWybaB9G2HyWbthKenJb0pLaEhoSwnGHHcJ3i1bVu77HcdhbWkqZx0NxSSlJ8TE+jNa30pNd7My37CqweBxYtM5D/y7uamX6dXGzYLUHgM1ZlshwiInybispTyS6Xd5HML8FbtuwmITkziQkpeMOCaPvYZNYvV9HKDo2kdQuA3G5a+YVHMdDWWkxjqeM0pJi2sQn1ygTLLZvXExCUmfi23mPRZ+hk1i3eL9jEZNIh841j8XeokIy1s5lwFGnAeAOCSMiKnjfUzqnuNiZ65Cd732NLFxdxoBu1ds8oFsIc1d4XwybMh0iww2xUYb8PZaMHd7b9e0thcwch7g2Qd/NEFpAZjFnVxZt26VULLdNTGbdmqU1ys2f/S3vvvo4BXk5XHnTw80YYeCISE2hKGN7xXLx1u1EdEypZX0m8cMG+iPEJrMjJ5eUxISK5eTEeJat3Vij3JI1G/j9tXeRlBDH5ef8ju7pqc0YZfPIys2nfULlm1dKQixLNmTUKLd4fQZn3P4USXEx/P20CfRITSYlIZY/jj+Kidc/RERoKEf0685R/XrUqBss4qIhr7Cyi5dXaElPce1XxpBbpUxuoSUu2lCwx2IMXDElnMQ4w8ylZWwJ0qwiQGFuJrEJ7SuWYxJS2Lahfh+oYhJSGD7+Qp64fgwhoeF07TeCbv2O9lWoPleQm0lMlWPRJj6FXzfW71jk7dxCVJu2fP7q9ezYupKUTv0Zc9qNhIVH+Spcn4qLNuTs9/zv3L7maySn0KlSxiGujbezuE/bGENakotN2z2+D9rPAunr/nyl3i00xtxrjIk1xoQaY74xxuw0xpx7gPKXGmPmGWPmffD2i00TbW1qOVebWu5JOfSIMdz9xDT+dv39vP/fp2tWagVqS5Vba73XHWpuaIaIfKf28Ku3s3eXdD587Db+e88NnHHcaP754IHucxq8ajsU+79G+nbqwGd3Xcnb//ozZ40Zxt+fehOA/N1FfLdoJZ/ceSVf3vsPivaW8MnsRc0QdTOqx1N93/PJWnj43b3c+WoxnZJdpCQE7+UnW0vDazt31qZodx5rFn3DZXd+w9/u/YHSvUUsnf1BU4fYjGo5FvW8tOg4ZWRuWc7gkWfzx+v/R2hYJD9/GcTnkvq8HRykTFgoXHBCBO//sJe9GrLYIjSkOzyh/O7eJ+K96WMv4Jq6Cltrp1prD7PWHnbyGRf8xjDrlpCYzK6dmRXLu7KziG+bVGf53v0PJWt7BgX5uT6LKVAVbd1OZFrlp+eIju3Zuy2L4hrrUyjeluWPEJtMctt4MrNzKpazsnNJSoirVqZNVCRREREAjBhyCGVlHnLzC5s1zuaQEh/L9pzKG/Nn5uTXuJTcJjKCqAjv5ISRA3pR5vGQU7ib2SvX07FdAm1jogl1uxk3pC+/rN9CsMrbDXFtKt/p9s+GeMtY4quUia+lTHEJrNvm0LtT9UvYwSQmvj35OZVXFApyMut9KXnjypnEtUsjKqYtbncovYdMIGP9Ql+F6nMx8e0pqHIsCnMzaRNXv2MRE9+emPj2dOg6CIBeQyaSuWW5T+JsDnmFloT9n/+7bS1lXFXKuCrKuFxw4fERzF9VxuJ1LT+rCN6bcjfXw18a0lncN/3xBOANa+0uH8TTYF179iPr1y3syNxKWWkpP//4JUOGjapWJvPXLd4MGrBx3UrKykppExNX2+5atKyPptPx3FMAiB8+iLL8AvZu30He3CVE9+hCZJc0TGgoqWdOIvPj6f4N9jfq170zW7ZnsTVrJ6VlZXw5az4jhw6oVmZnbl7F82LZ2o041hIX0zLGKVbVv0sqm7Oy2bozh9KyMr6Yt5TRg3pXK7Mzr6DiWCzZkIF1LPHRUXRoG8fi9RkUlZRgrWXOyg10a1/3h7FAl5Hl0C7OkBBjcLtgUHc3yzdWf0NbvtHDob28ncBOyYaiEijYA9EREBHmLRPihp5pLnbkHPDrVANaapcB5GRtJHfnFjxlJayY9wk9B42tV93YtqlsW7+I0pIirLVsXDmLdu27+zhi32nfufqxWDn/E7oPqN+xiI5LIiahPbsyvfc03rRqFolBfCw2Zzq0i3fRNtb7GhnSK4SlG6q/RpZuKOPwvt5RbJ1TXBSV2IoPVGePCyczx+G7X5RSbEkaMmbxI2PMSqAIuMwYkwT4fcqX2x3COZdcwwO3/g3H42HksZPp2Kk7337+LgBjJp7GvFnfMPPbT3G7QwgLD+fPV99dcYnh6QduYOXS+RTm53LVRSdwylmXMmr8KX5sUeMNfvUBEkcPI6xdAmM3fM+a2x7DhHr/xZunvknWZ9+TdPxojln5FZ6iIhZffAMA1uNh6RW3MeyT5zBuNxkvTaNw+Vp/NuU3C3G7ueb8M7j87idwHIeTjjmS7umpTPvqBwCmjB/J9DkLmfbVD7jdbiLCQrnz8gsrnhc3PfoC81esIbegkBP/ciOXnDaJk8cc5c8mNVqI2811Z53Anx95FcexnDxiCD1Sk3nn+7kAnD76cL5esJy3v59HiNtFeGgI/7nkNIwxDOiaxrGH9uPsO57B7XbRJ70DU0YO9XOLGs+x8MGPpVw8KQyXgbmrPGTmWI7o5+0czl7uYeVmhz6dLNeeHU5JGbzzXQkAMVGGM8d66xkDi9d5WLE5eDuLLncI48+6mTcfuRjreBg4YgpJqT1Z8P0bABw6+mwK83bw0l1T2FtciDEu5n7zMpfc8ikduw6i96HH8cIdv8PlDiElvS+DR57p5xY1nssdwrgzbmbaExfjOB4GHDmFdqk9+eUH77EYPPJsduft4NV7p1BSfizmf/syF9z0KeGRbRh3+r/45KWr8ZSVEt8unYl/uNvPLWo8x8K07/fyp8mRuFwwZ3kp23c5HHWI971k5tIylm/00Lezm5v+GEVJqeWNb/YC0LWDi8P7hLJtp4drzooE4ONZJazY1MIzjK1gzKKxDRibZoxJAPKttR5jTBQQa63dfrB6M1cUBPcAuCaUM/Awf4cQMI6e84S/QwgIYfk7/B1CwPj3ylP8HULA6Ncn0t8hBIzS4L6dZZNatqLlDZX5LR7+Wxu/DxzOvu3SZuvjJN481S/trXdm0RjjBkYCXYwxVes92ORRiYiIiAQBf44lbC4NugyN97LzEiB4r72IiIiISL01pLOYZq0N7pvviYiIiDQhY1r+mMWGtPAzY0yDvnhaRERERIJbQzKLs4H3jbcLXYr3tpzWWhu832skIiIi8ltozGI1DwBHAktsQ6ZQi4iIiEjQakhncQ2wVB1FEREREa/W8N3QDeks/gp8Z4z5DNi7b6W1VrfOEREREWmhGtJZ3FD+CCt/iIiIiEgLV+/OorX2VgBjTIx30eo28iIiItKqtYabctf7Qrsx5hBjzEJgKbDMGDPfGNPfd6GJiIiIiL815DL0VOAqa+23AMaYY4BngaOaPiwRERGRIKCbclcTva+jCGCt/Q6IbvKIRERERCRgNCSzuN4Y8y/g1fLlc/FOeBERERFplTRmsboLgSTgPeD98t8v8EVQIiIiIhIYGjIbOge43BgTCziaDS0iIiKtXiu4KXdDZkMPKJ8NvYTK2dCH+C40EREREfG3hoxZfIaas6GnotnQIiIi0koZozGLVWk2tIiIiEgro9nQIiIiIo2lMYvVVJ0N/R7QDs2GFhEREWnR6pVZNMa4gXestcf6OB4RERGRoKH7LJaz1nqAPcaYOB/HIyIiIiIBpCFjFouBJcaYr4Dd+1Zaay9v8qhEREREgkEr+G7ohnQWPyl/iIiIiEgr0ZBvcHnZl4GIiIiISOA5aGfRGLMEsHVtt9YObNKIRERERIJFK5jgUp/M4onlP/9S/nPffRbPAfY0eUQiIiIiEjAO2lm01m4CMMaMsNaOqLLpOmPMT8BtvgpOREREJJAZTXCpJtoYc7S19kcAY8xR1PPr/gbs/qkxsbVIzpwn/B1CwPhx+F8OXqgVOOq2cf4OIWDEdzvD3yEEjFPiv/N3CIHDOv6OIGCsizjK3yFIK9SQzuJFwAvl91q0QB7eb3URERERaZ00ZrGStXY+MMgYEwsYa22e78ISERERkUBQ786iMSYFuAtItdYeb4zpBxxprX3eZ9GJiIiIBDDjavljFhvSwpeAL4DU8uXVwJVNHI+IiIiIBJCGdBbbWWvfBhwAa20Z4PFJVCIiIiLBwJjme/hJQzqLu40xiZTfoNsYcwTeSS4iIiIi0kI1ZDb0VcCHQLfy+ysmAaf5JCoRERGRYNAKxiw2pLO4HHgf77e2FAD/wztuUURERERaqIZ0Fl8B8vHOiAY4G+9X/53e1EGJiIiIBAU/jiVsLg3pLPa21g6qsvytMWZRUwckIiIiIoGjIZ3FhcaYI6y1swGMMcMBfY+fiIiItFqt4T6LB+0sGmOW4J0BHQr80RizuXy5M95xjCIiIiLSQtUns3iiz6MQERERkYB00M6itXZTcwQiIiIiEnRMy78M3fJbKCIiIiKN1pAJLiIiIiJSlavl3zpHmUURERERqZMyiyIiIiKNZDRmUURERERaM2UWRURERBpLYxZFREREpDVTZlFERESksTRmUURERERaM2UWRURERBrLaMyiiIiIiLRiyiyKiIiINJar5efdWn4LRURERKTRlFkUERERaSzNhhYRERGR1qxFZBZnLlrB/a++h+M4nHLMEZw/eXy17fOWr+EfDz5Hx6REAMYcPpBLTp3I3pJSLrn9UUrLyvB4HMYNG8T/nXaCP5rQZGb9sowHXnkXx3E4ecwIzjt5QrXt85ev5ur7nyE1ed+xGMzFU7xtvv3pV/lx4VISYmN4876bmj32pjTw2btIPuEYSrKymTHkpFrL9HvoRpInjsZTVMyii64jf+FyAJImjKTfgzdi3C62vPAO6+57tjlD94mQbv2IOvZ0cBn2/jKTvbO/rLY9fPixhPU/HADjcuNKbE/eI//EFu/BhEcSdcI5uJNSwcLuT1/Fs3WDP5rRJLq1h/FDXBgDi9ZbZq20NcqMH2Lo3sFQ5oGPfnbIzPGuP7ynYXB378zHX9Zb5q6uWTeYzPplKQ++9DaO4zB57NGcd8rEatvnL1vFNfc9SWpyOwCOGTaEi087sV51g82sX5bx4Mtv4ziWyWNHcN7Jx1XbPn/Zaq65/6kqx2IwF0+ZBMDtT7/CTwuWkBAbwxv339zssTe1XmkuTjoyBGNg7ioP3y/y1Chz0pEh9E53UVoG73xfyrbsyteCMfC3U8LI22N5+YvS5gxdfCToO4sex+Gel97hiesvI6VtPH/81wOMOnQA3dLaVys3pHc3Hr7m/6qtCwsN4ekb/0pURDhlZR4uuu0RjhrUjwE9uzRjC5qOx3G498W3efyGv5GcGM95N97LyKED6JbWoVq5wX168NA//1yj/qTRR3D6caO55clXmitkn8l4+T02Pvkag1+4p9btSRNHEd2jC9/1nUD88EEc8vgtzBxxBrhc9H/0ZuYcfwHFGZkcPftdMj+eTuGKdc3cgiZkDFETzqTwzUdx8nOJOf9aStcsxsneXlFk75yv2TvnawBCewwg/PCx2OI9AESOP53S9cvZ/f5z4HJjQsP80oymYAwcN9TFG9855BfBBeNdrNlm2ZlfWaZ7B2gbY3j6U4fURJg41MXLXzskxcHg7oYXv3LwOHDWKBdrt1lyCv3Xnt/C4zjc98IbPHbjlSQnJnD+9Xcz8rCBdEtLrVZucN+ePHjtXxtVN1h42/Mmj914ubc9N/yHkUMH1nrufPDav9Sof+LoIzn9uGO49YmXmili3zEGTh4RwvOflpK32/LXU8JYsckhK7eyM9g73UW7OMP9b5eQnmw45ehQnvygpGL7iEPcZOVawoP3VNEw+rq/wLds3SbSU5JIS25HaEgIE444lO/nL6lXXWMMURHhAJR5PJR5PEF9u6RlazeS1j6Jjinlx+LIocyYt7je9Q/t25PYNtE+jLD57PpxHqW78urcnjJ5HFtf+x8AuXMWERoXS3j7JOKHDWTPuk0UbcjAlpay7a1PSDlpXDNF7Rvu1C44OTtwcrPB8VC6Yj5hvQbVWT6s32GULJ9XvhBBSHoPShbN9C47HuzeomaI2jdS20JOAeTuBseB5ZstPTtWf9H36mhYstH7xrgtGyJCIToCEmNga7alzAPWwuYdlt5pwXvCWL52A2kpyXRMSSI0JITxRx3GjLmLfF43EC2vOHdWac+8+rdnSN+exEa3jHNnepIhO9+yq8DicWDROg/9OlfvKvTr7GLBGm+2cUuWJTIMYiK922KjoU+6i7mramYjJXjVu7NojLnCGBNrvJ43xiwwxkw4eE3fytqVR0pifMVyctt4snJqdhKWrN3I2dffw+X3PM26jF8r1nsch99ffy/j/3wjww/pzSE9ujRD1L6xIyeXlMSEiuXkxHh25OTWKLdkzQZ+f+1dXPGfJ1i3ZVszRhg4IlJTKMqozKwVb91ORMeUWtZnEtExxR8hNhlXm3ic/JyKZacgBxMTV3vhkFBCuvWjdNVCANzx7bB7Coma9AdiLrieqOPPgSDOLMZEQn5RZYakYE/lm9w+bSIN+XuqlCnyltmR530jjQyDEDd072CIjWquyJte1q79zxcJtZ8vVq/nnGtu58q7H2V9+fmivnWDRY32tE1gx67cGuWWrNnAOf+8gyvvfqziWLQ0sdGGvMLK53/ebktstKlRJreOMicdEcpnP5dhg3uERsMYV/M9/KQhf/lCa20+MAFIAi4A/lNXYWPMpcaYecaYeS++9+lvDPNAaj4j988O9umSzkeP3MIbd1/LGceN5OoHn6vY5na5+O/d/+TTx25l2bpNrA3iE0DtL87qB6N3l3Q+fOw2/nvPDZxx3Gj++eDUZokt0JhaUsjW2trvxB/sZ73akl91NCm050DKMtZXXILG5cLdPp29C3+g4MW7saUlRBzp98+IPlVXrjC7AGavsJx9jIuzRrnIyrU4TrOG1rRqeQ7s3/beXTvxwRN38fp9/+L0iWO45v6n6l03uNT2PrLfubNrOh88fgev33uT91g88HRzBdes6vN/rKtMn04uCostW3cG+TlTamhIZ3Hf8+ME4EVr7SIO8Lyy1k611h5mrT3sglN9N2kkuW08mdm5FctZu3JJiq+eNWkTFVFxufnowf0p8zjkFlQfaBQTHcXQvj2YtXilz2L1Ne+xqMwgZWXnkpSw/7GIJCoiAoARQw6hrMxDbn6QDrr6DYq2bieyyrjWiI7t2bsti+Ia61Mo3pbljxCbjFOQiyu2MmviiknAFtZ+iT6s71BKls+tVtfJz8WzbSMApSsXEJLSyafx+lJBEcRGVp62YqK866qXscRGVSkTWVlm0QbLC186vPatQ1EJ7Aril05y4v7nixzaJcRXK1P9fDEAj8d7vqhP3WCS3Dahent25dDuIOdOTws9d+bttsS1qXz+x0Ub8nfbGmXiaynTOcVFv05urj0rnLPHhtI91cWZx4Q2W+x+Y0zzPfykIZ3F+caYL/F2Fr8wxsQAfv9c3a9bJ7Zs38HWrGxKy8r4cvYCRg09pFqZnbn53qwRsHTdJhzrENcmmpz8Qgp2ezMoxSUl/LxsNV06JDd7G5pKv+6d2bI9i61ZO73HYtZ8Rg4dUK3Mzty8imOxbO1GHGuJi2kZY20aIuuj6XQ89xQA4ocPoiy/gL3bd5A3dwnRPboQ2SUNExpK6pmTyPx4un+D/Y082zbhSkjGFZcILjehfYdSsqaWsazhEYR06klplW12dz5OQQ6utt7XRUiXPnh2/lqzbpDYtgsSYiAu2vulC/06GdZsrf5GuHqrZUAX70k5NRH2lsLuYu+2KO9nTmKjoE+aYfmm4M2g9O3ehS3bs9hWfr74auY8Rh1WfSxrdrXzxQYcxyEuJrpedYNJ3/JzZ7X2DB1YrUx2Kzl3ZuywJMYaEmIMbhcM6u5m+ebqb/XLNzkc2tMNQHqyobjE+4Hqi7ll3P3GXu55cy9vTC9l3TaHt77TbOiWoCGzoS8CBgPrrbV7jDGJeC9F+1WI280150/hb/c8hcdxmDz6CLqndeDdr38E4LRjj+abn39h2tc/4Xa7CA8N5a6/no8xhp25efz76ddxHAfHWsYPH8LIQw85yF8MXN5jcQaX3/0EjuNw0jFH0j09lWlf/QDAlPEjmT5nIdO++gG3201EWCh3Xn5hxeWWmx59gfkr1pBbUMiJf7mRS06bxMljjvJnkxpt8KsPkDh6GGHtEhi74XvW3PYYJtT7dN889U2yPvuepONHc8zKr/AUFbH44hsAsB4PS6+4jWGfPIdxu8l4aRqFy9f6sym/nXXY89VbtDnrr2BclCyehbPzV8KGjASgZKH3+RHWazBlG1ZAaUm16kVfvk305AvAHYKTu5M9nwTvbHlr4csFDmeNduEqv3XOznwYUn47nIXrLOt+hR4dLH+e5L0tyMc/V75RThnhIjIMPBa+mO9QHMTvgyFuN1dfeBaX3/VI+fliBN3SU3nvq+8BOHX8aKbPXsC0r77H7XITHhbKHVdcgjGmzrrBKsTt5uoLzuLyux7ztmfMUeXHYgYAp44fxfTZC5n29QzcLpf3WFx+UZVz5/MsWL7ae+687HouPe1EJo8d4c8mNZpj4cOZZVx4fCguA/NWecjKsQzv6+0czlnhYdUWhz7pLq45M6zi1jmtWiv4uj9jGzAeyxgzEOhClU6mtfa9g9UrmPd58H78bmKOK+jvVtRkfhxe8xYUrdFRtwX3bOum9FS3x/wdQsC4rPcP/g4hcFi/X8QKGP+ZF5wf4H3lP5dE+H24bPHHTzVbHyfixD/7pb317rkYY14ABgLLqLz8bIGDdhZFREREWqRgvudePTUkzXWEtbafzyIRERERkYDTkM7iLGNMP2vtcp9FIyIiIhJM/Hj/w+bSkM7iy3g7jNuBvXhvm2OttQMPXE1EREREglVDOosvAH8AlhAAt8wRERER8btWMBu6IZ3FzdbaD30WiYiIiIgEnIZ0FlcaY/4LfIT3MjRQv1vniIiIiLRIATYb2hgzEXgEcAPPWWtrfDWzMeYY4GEgFNhprR19oH02pLMYibeTWPWLYXXrHBEREZEAYIxxA08A44EMYK4x5sOqk5ONMfHAk8BEa+1mY8xBv7qu3p1Fa63fv61FREREROo0DFhrrV0PYIx5EzgZqHonm98D71lrNwNYa7MOttN6j8o0xqQZY943xmQZYzKNMdOMMWkNaoKIiIhIS2Jczfc4uI7AlirLGeXrquoFJBhjvjPGzDfG/PFgO23IFJ4XgQ+B1PI//FH5OhERERHxMWPMpcaYeVUel+5fpJZq+38dYQgwFJgEHAf8yxjT60B/tyFjFpOstVU7hy8ZY65sQH0RERGRlqUZJ7hYa6cCUw9QJANIr7KcBmyrpcxOa+1uYLcxZgYwCFhd104bklncaYw51xjjLn+cC2Q3oL6IiIiI+M5coKcxpqsxJgw4C+9V4ao+AEYaY0KMMVHAcGDFgXbakMzihcDjwEPlyz+VrxMRERFpnQLoptzW2jJjzF+BL/DeOucFa+0yY8yfyrc/ba1dYYz5HFiM90tWnrPWLj3QfhsyG3ozMLnRLRARERERn7LWfgp8ut+6p/dbvg+4r7771GxoERERkUayxjTbw180G1pERERE6qTZ0CIiIiKNVb/7HwY1zYYWERERkTo1dja0BWai2dAiIiLSmrWCzGK9OovlX0x9l7VWs6FFREREWpF6dRattR5jTJIxJsxaW+LroERERESCgT9nKTeXhlyG3gj8ZIz5ENi9b6W19sGmDkpEREREAkNDOovbyh8uIMY34YiIiIgEEY1ZrGStvdWXgYiIiIhI4Kl3Z9EY0wu4GuhStZ61dmzThyUiIiIigaAhl6HfAZ4GngM8vglHREREJIhogks1Zdbap3wWiYiIiIgEnIN2Fo0xbct//cgYcxnwPrB333Zr7S4fxSYiIiIS2Fya4AIwH+83tuzLs15TZZsFuh1sB66FPzY8shYqpGd/f4cQMI66bZy/QwgIM2/+xt8hBIwOX7r9HULA2Bitc8U+xZ4wf4cQMFyuln/JUwLPQTuL1tquAMaYCGttcdVtxpgIXwUmIiIiEuhaw025G5I7nVnPdSIiIiLSQtRnzGJ7oCMQaYwZQuXl6FggyoexiYiIiAQ23ZQbgOOA84E0oOpX+xUAN/ggJhEREREJEPUZs/gy8LIxZoq1dlozxCQiIiISFKwyi5WstdOMMZOA/kBElfW3+SIwEREREfG/hnzd39N4xyiOwfstLqcBP/soLhEREZHAp9nQ1Rxlrf0jkGOtvRU4Ekj3TVgiIiIiEgga8nV/ReU/9xhjUoFsoGvThyQiIiISHDRmsbqPjTHxwL14v9UFvJejRURERKSFakhn8X7gz8BIYBbwA/CUL4ISERERCQqtYMxiQzqLL+O9t+Kj5ctnA68AZzR1UCIiIiISGBrSWextrR1UZflbY8yipg5IRERERAJHQzqLC40xR1hrZwMYY4YDP/kmLBEREZEgoAkuYIxZAlggFPijMWZz+XJnYLlvwxMRERERf6pPZvFEn0chIiIiEoSsJriAtXZTcwQiIiIiIoGnIWMWRURERKSqVjBmseW3UEREREQaTZlFERERkUaytPwxi8osioiIiEidlFkUERERaSSrMYsiIiIi0popsygiIiLSWMosioiIiEhrpsyiiIiISCO1hm9wUWZRREREROqkzKKIiIhII2k2tIiIiIi0ai0is/jThu3cP30hHmv53YBuXDC8T7XtL/+8is9WbALA41g27Mrnm8tOJi4y7KB1g81PS9dw79uf4zgOvzv6UC6cOLLa9rmrNvD3J98ktV08AOOG9OX/TjwGgFe/nsX7Py7AGOjZMYVbzzuZ8NDQZm5B0wnp1o+oY08Hl2HvLzPZO/vLatvDhx9LWP/DATAuN67E9uQ98k9s8R5MeCRRJ5yDOykVLOz+9FU8Wzf4oxm/2cBn7yL5hGMoycpmxpCTai3T76EbSZ44Gk9RMYsuuo78hcsBSJowkn4P3ohxu9jywjusu+/Z5gzdJ9Yvm8HXb9+J4zgMGnE6R068tNr27O3r+OTlG8jcsoxRk//O8AkXla9fzwfP/b2iXO7OLYw86XIOH3d+c4bfpH6ZP5uXpj6C4ziMnXAip5z+h2rb587+gbdfew5jDG63m/MuuZw+/QcB8NcLTyMiMgqXy4Xb7ebuh5/3RxOazOIFs3j92QdwHIfR40/mxNPOq7Z9wZzvmfb6M7hcBpfLzTkXX0WvfoPJ3pHJ1IdvIS83G2MMY477HRNOOstPrWgaPTsaTjwiBJfLMHeVhxmLPTXKnHiEm97pbkrKLNNmlLEt2wJwzRlh7C21OBYcB578sLS5wxcfCPrOosex3PP1Ap48fRQpMVGc+9rXjO6eSrd2sRVlzhvWm/OG9Qbg+3XbeH3eauIiw+pVN5h4HIe73/iUp6/8AykJsZxz97OMHtib7qnJ1coN6dmJx/56TrV1mTn5vDF9Du/d8hciwkK5ZurbfD53KScfNaQ5m9B0jCFqwpkUvvkoTn4uMedfS+maxTjZ2yuK7J3zNXvnfA1AaI8BhB8+Flu8B4DI8adTun45u99/DlxuTGiYX5rRFDJefo+NT77G4BfuqXV70sRRRPfownd9JxA/fBCHPH4LM0ecAS4X/R+9mTnHX0BxRiZHz36XzI+nU7hiXTO3oOk4jocv37iNs654kZiEFF66+zR6DhxLu9QeFWUiouIZf+aNrP7lm2p1E9t348KbPqjYzxPXjaLX4PHNGn9TcjweXnjqQW684yESE5O5/u8Xc9jwo0nr1LWizIBBQzls+NEYY9i0YS0P33MzDz3934rtN9/1KLFx8X6Ivmk5Hg+vPHMv/7z1cdomJnPL1ecxZNhIOnbqVlGm38DDGTJsFMYYNm9cw5P33sB/nnwHt9vN2RdeQZfufSjas5t//+OP9B80rFrdYGIMTD4qlBc+LyF/N1w2OZSVmx2ycm1FmV5pLhJjXTzwTgnpSYaTjwrhqY8qO4XPfVrKnr3+iN5PNMEl8C3dvou0hDakxbch1O3iuD7pfLdua53lv1ixmYl9OzWqbqBbumEr6cltSUtqS2hICMcddgjfLVpV7/oex2FvaSllHg/FJaUkxcf4MFrfcqd2wcnZgZObDY6H0hXzCes1qM7yYf0Oo2T5vPKFCELSe1CyaKZ32fFg9xY1Q9S+sevHeZTuyqtze8rkcWx97X8A5M5ZRGhcLOHtk4gfNpA96zZRtCEDW1rKtrc+IeWkcc0UtW/8unExCcmdiU9Kxx0SRr/DJ7FmcfVOYXRsIh26DMTlrvuz9KaVs4hvl05cYkdfh+wza1evIKVDGintOxISGspRo45l7uwfq5WJiIzClL8R7i0uhhb6Hbjr1ywjpX0ayeXHYvjICSz4eUa1MlWPRUlxUUUHIb5tO7p0916RioyKJjWtKzm7djRvA5pQWpIhO9+SUwAeBxavd+jbqXpXoV9nFwvXerONW3ZYIsIgJtIf0UpzqXdm0Rhzai2r84Al1tqspgupYXYUFNE+JqpiOblNFEt/za61bFFpGTM3bufacYc2uG4wyMrNp31CZVY0JSGWJRsyapRbvD6DM25/iqS4GP5+2gR6pCaTkhDLH8cfxcTrHyIiNJQj+nXnqH49atQNFq428Tj5ORXLTkEO7tQutRcOCSWkWz/2fPkWAO74dtg9hURN+gPu5DQ82zez5+t3oLSkGSJvfhGpKRRlVGZci7duJ6JjSi3rM4kfNtAfITaZgpxMYhLaVyzHxKewbcPiBu9n+bxP6Hf4iU0ZWrPblb2DxKTKqw6J7ZJYu2p5jXI/z/yeN155hrzcHK77932VG4zhzpuvwgDHHn8yx048uRmi9o2c7B20bZdSsdw2MZl1q5fVKDdv1re8++qT5OflcNW/HqyxfUfmNjatX0X3Xv19Gq8vxUUZ8nZXZhHz9ljSk6p3FmOjqFYmfw/ERhsKiiwWuGCid/jSzys9zF3lNEvc/tQaJrg05DL0RcCRwLfly8cAs4FexpjbrLWvVi1sjLkUuBTg0XOP58JRh/72aGvhfWpWZ+pICc9Y9yuDUtsRFxnW4LrBoGZrwOyXCejbqQOf3XUlURHh/LBkNX9/6k0+uv1y8ncX8d2ilXxy55XEREVwzTNv88nsRUw6ou5sXECr7d9Y2wECQnsOpCxjfcUlaFwu3O3T2fPV23i2bSTy2NOJOHICxTM+9lm4/lTbc95aW/ulFVvHQQwatcTfwNe8p6yEtYumc8wp/2iimPyjtvNfbcdi2FGjGXbUaJYv/YW3XnuWf935CAC33fsUbRPbkZebwx03XUlqWmf6HTLYx1H7Ru3vBTXLHXbkGA47cgwrly1g2uvPcO3tT1RsKy7aw2P3XMc5F19FZFQbX4bb/PY/PAc4NTzzcQkFeyA6Ai6cGMqOPMvG7cF+3pCGdIcdoK+1doq1dgrQD9gLDAeu3b+wtXaqtfYwa+1hvuooAiTHRLG9YE/FclbhHpLaRNRa9suVlZegG1o3GKTEx7I9J79iOTMnv8al5DaREURFhAMwckAvyjwecgp3M3vlejq2S6BtTDShbjfjhvTll/VbmjX+puQU5OKKTahYdsUkYAtrvxQb1ncoJcvnVqvr5Ofi2bYRgNKVCwhJ6VRr3ZagaOt2ItMqs20RHduzd1sWxTXWp1C8zW8XEZpETEJ7CnIqs6UFuZnExCcfoEZN65bOIKVTf6Jj2zV1eM0qMTGZ7B2V/8/snTtIaFt3m/odMpjM7dvIz8sFoG2it2xcfALDjhzFutU1s5LBom1iMrt2ZlYs78rOIr5tUp3l+/Q/lKztGRTk5wJQVlbGY/+5lqNGH8dhR47xdbg+lbfHEhdd2RuMizLk76ne2cvfTbUysVFQUF5m31vq7mJYvskhrV3Lz7pZTLM9/KUh/8Uu1trMKstZQC9r7S7Ab9Od+rdPYEtOIVtzd1Pqcfhi5RZGd0+tUa5gbynzM3ZwTJVt9a0bLPp3SWVzVjZbd+ZQWlbGF/OWMnpQ72plduYVeLNGwJINGVjHEh8dRYe2cSxen0FRSQnWWuas3EC39nWfLAOdZ9smXAnJuOISweUmtO9QStbUcrkxPIKQTj0prbLN7s7HKcjB1dbbiQjp0gfPzl+bK/Rml/XRdDqeewoA8cMHUZZfwN7tO8ibu4ToHl2I7JKGCQ0l9cxJZH483b/B/kYdOg9gV9ZGcnduwVNWwvK5n9Bj4NgG7WPFvE/od/gkH0XYfLr36sP2bVvI2r6NstJSZs74msOGj6hWZvu2jIrzxfq1qygrLSUmNo7i4iKK9nh7BcXFRSxeOJf0zsE5oQOga89+ZP66hR2ZWykrLWXOD18yZFj1O0lk/rql4lhsXLeSsrIy2sTEYa3l+cduJzW9KxNPPqe23QeVrTss7WINCW3A7YKB3Vys2Fz9UvKKzQ5DergBSE8yFJdCQRGEhkBY+Q00QkOgR0cXmTkt/zJ0a9CQy9A/GGM+Bt4pX54CzDDGRAO5TR1YfYW4XFw7bgh/mTYDx7FMHtCV7u3iePcX74zN0wZ3B+DbNVs5onN7IsNCDlo3WIW43Vx31gn8+ZFXcRzLySOG0CM1mXe+92bNTh99OF8vWM7b388jxO0iPDSE/1xyGsYYBnRN49hD+3H2Hc/gdrvok96BKSOH+rlFv4F12PPVW7Q5669gXJQsnoWz81fChnjfAEoW/gBAWK/BlG1YUWM8YtGXbxM9+QJwh+Dk7mTPJ680exOayuBXHyBx9DDC2iUwdsP3rLntMUyo93WweeqbZH32PUnHj+aYlV/hKSpi8cU3AGA9HpZecRvDPnkO43aT8dI0Cpev9WdTfjOXO4QJZ97MW49ejHU8DDxqCkmpPVk44w0Ahow6m8K8Hbx89xT2FhdijIt501/m4n9/SnhkG0pLitiwYibHnXObn1vy27ndIVz4p6u46+arcByHY8ZPIr1zN7769H8AjD/hFObM/I4Z0z/H7Q4hLCycK6+9FWMMebm7uP8O7/PEcTyMGD2ewUOP8GNrfhu3O4Q/XHoN991yOY7jMGrcSaR16s70z6YBMPb4KcybOZ0fv/2UkJAQQsPC+cs1d2KMYfXyX5j53Wekde7Bv670dhZPO/cyBh024kB/MmA5Fj6cVcYFE0MxxjB/tYesXMuwPt7c0s8rHVZtceid5uIfp4dRWmaZ9kMZAG0i4dxx3t6iywWL1jms2dryL0G3hjGLxtZzDJLxDmyaAozAO2LhR2CarccOdj97U8t/ttSTq2fwDnxuasWzfvB3CAFh5s3fHLxQK5H1Zf1n77d0Q9KCd0ZtUyv2BO+tq5rahz8F71ApX7jronC/TzTYsWxOs/VxkvoP90t7651ZLO8Uvlv+EBEREZEgnhhbX/XOnRpjTjXGrDHG5Blj8o0xBcaY/IPXFBEREZFg1ZAxi/cCJ1lrV/gqGBEREZFgYoP/+00OqiEtzFRHUURERKR1aUhmcZ4x5i3gf3jvrwiAtfa9pg5KREREJBjYVjBmsSGdxVhgDzChyjoLqLMoIiIi0kI1ZDb0Bb4MRERERCTYtIb7LB60s2iM+ae19l5jzGPU8sWq1trLfRKZiIiIiPhdfTKL+ya1zPNlICIiIiLBxp/f2dxcDtpZtNZ+VP7rYmvtQh/HIyIiIiIBpCEX2h80xqw0xtxujNF31omIiIi0Ag2Z4DLGGNMeOAOYaoyJBd6y1t7hs+hEREREAlhrmODSoBZaa7dbax8F/gT8Atzsi6BEREREJDDUO7NojOkLnAmcBmQDbwL/8FFcIiIiIgFPN+Wu7kXgDWCCtXabj+IRERERkQDSkDGLR/gyEBEREZFgo1vnAMaYt621ZxhjllD9ptwGsNbagT6LTkRERET8qj6ZxSvKf57oy0BEREREgk1rmA1dn5ty/1r+c5PvwxERERGRQFKfy9AF1PKd0FReho5t8qhEREREgoDGLALW2pj67MgYk2CtzfntIYmIiIhIoGjIrXMO5hvg0Cbcn4iIiEhAaw1jFpuyhS0/DysiIiLSyjRlZrG2cY0iIiIiLVZrGLPY8nOnIiIiItJoTZlZbPldaxEREZEqNGaxCmPMqwdZN65JIhIRERGRgNGQ7nD/qgvGGDcwdN+ytXZXUwUlIiIiIoGhPjflvh64AYg0xuTvWw2UAFN9GJuIiIhIQGsNE1zqM2ZxrbU2xhjztrX2jMb8kUdib21MtRYpd2WJv0MIGPHdGvV0anE6fOn2dwgBI3lCb3+HEDCevXuOv0MIGPFtI/wdQsAoKNjr7xACTLi/A2gV6nMZ+vrynz18GYiIiIhIsLHGNNujPowxE40xq4wxa40x1x2g3OHGGI8x5rSD7bM+mcVsY8y3QFdjzIf7b7TWTq7HPkRERETEh8rnkzwBjAcygLnGmA+ttctrKXcP8EV99lufzuIkvF/j9yrwQEOCFhEREWnJrA2oMYvD8A4fXA9gjHkTOBlYvl+5vwHTgMPrs9ODdhattSXAbGPMUdbaHQ0KWURERESaS0dgS5XlDGB41QLGmI7A74CxNFVnsYoXjDF1fqWfLkeLiIhIa2Ob8cvwjDGXApdWWTXVWlv1zjS1pTn377s9DFxrrfWYeo6DbEhncT3QHnitfPlsYCP1vN4tIiIiIo1X3jE80G0LM4D0KstpwLb9yhwGvFneUWwHnGCMKbPW/q+unTakszjEWjuqyvJHxpgZ1tobGrAPERERkRYjwO6zOBfoaYzpCmwFzgJ+X7WAtbbrvt+NMS8BHx+oowgN+waXJGNMtyp/oBuQ1ID6IiIiIuIj1toy4K94r/quAN621i4zxvzJGPOnxu63IZnFK4HvjDHr8V7/7kr16+YiIiIirUqAZRax1n4KfLrfuqfrKHt+ffbZkM5iLHAI3k7iZOAoYGcD6ouIiIhIkGnIZeh/WWvzgRi8N3t8GnjKJ1GJiIiIBAGLabaHvzSks+gp/zkJeNpa+wEQ1vQhiYiIiEigaMhl6K3GmGeAY4F7jDHhNKyzKSIiItKiBNqYRV9oSGfvDLyzayZaa3OBtsA1vghKRERERAJDvTOL1to9wHtVln8FfvVFUCIiIiISGBpyGVpEREREqrBWl6FFREREpBVTZlFERESkkTTBRURERERaNWUWRURERBpJmUURERERadWUWRQRERFpJGUWRURERKRVU2ZRREREpJF0n0URERERadWUWRQRERFpJEdjFkVERESkNVNmUURERKSRNBtaRERERFq1FpFZ7NYexg9xYQwsWm+ZtdLWKDN+iKF7B0OZBz762SEzx7v+8J6Gwd29nwp+WW+Zu7pm3WDSK93FySNCMQZ+XuHhu1/KapSZPCKUPp1clJbB29+WsHWnJcQNfzo5nBAXuFywZL2Hr+bVrBtM9LyotH7ZDL5++04cx2HQiNM5cuKl1bZnb1/HJy/fQOaWZYya/HeGT7iofP16Pnju7xXlcnduYeRJl3P4uPObM/wmM/DZu0g+4RhKsrKZMeSkWsv0e+hGkieOxlNUzKKLriN/4XIAkiaMpN+DN2LcLra88A7r7nu2OUP3mdPHRtK/awglZfDqZ3vYkuWpUSYxzsWFJ0YRFWHYkunh5U/34HHqXz8Y9OxomHRECC5jmLfaw4zFNdsxabib3uluSsss034oY1u297xw9elh7C21WAuOhSc/LG3u8JtU385upoyOwOUyzFpawlfzSmqUmTI6nP5dQykptbz2ZREZO5x6121pNBs6CBgDxw118dYMh6mfO/TrbGgXW71M9w7QNsbw9KcOn85zmDjU2+ykOBjc3fDiVw7PfeHQo4MhoY0fGtFEjIHfHR3K85+U8MBbexncw01yQvUncZ9OLtrFGe59Yy/Tvi/hdyPDACjzwNQP9/Lwu95H73Q3nZKD9wWg50Ulx/Hw5Ru3ccZfn+OSf3/C8rkfs3Pb2mplIqLiGX/mjQw79qJq6xPbd+PCmz7gwps+4Pwb3iM0LJJeg8c3Z/hNKuPl9/j5xIvr3J40cRTRPbrwXd8JLPnzvzjk8Vu8G1wu+j96Mz+fdDHfD5xE6lkn0qZv9+YJ2of6dw0hKcHFLc8X8N8v93DW+Mhay50yKoLp8/Zy6/MF7Cm2HDUgrEH1A50xcNKRobz8ZSmPvFfCwG4ukuKrn/96pbloF+fiwXdL+N9PZUw+qnqu5fnPSnn8g9Kg7ygaA6ePieSp/+3hzlcKGdo7lPZtq3cV+nUJITnBzW0vFfLmN8WcOS6y3nUlOAX9fzG1LeQUQO5ucBxYvtnSs+N+L/KOhiUbvZ8At2VDRChER0BiDGzNtpR5wFrYvMPSOy14O0jpyS525lt2FVg8Dixa56F/F3e1Mv26uFmw2vuJeXOWJTIcYqK820rKE4lul/cRzLk0PS8q/bpxMQnJnYlPSscdEka/wyexZvE31cpExybSoctAXO66LzZsWjmL+HbpxCV29HXIPrPrx3mU7sqrc3vK5HFsfe1/AOTOWURoXCzh7ZOIHzaQPes2UbQhA1tayra3PiHlpHHNFLXvDOwRypxl3szPxl89RIYbYqNrPtd7pYewcLW3EzRnWQkDe4Q2qH6gS2tn2JVvySkAjwOL1zv07VT97bFvJxcL13rPnVt2WCLCICY4+8YH1Lm9m515Dtn53veR+atLGdC9+nlhQPcQfl5R/n/f7iEyDGKjTL3qSnCqd2fRGHNFfdY1t5hIyC+q7NYU7Kn5Am4TacjfU6VMkbfMjjxITzJEhkGIG7p3MMRGNVfkTS8uGvIKK9uZV2hrnLjjog25VcrkFlriyssYA1eeFs7N50WwOsPDlqzg7S7qeVGpICeTmIT2Fcsx8SkU5GQ2eD/L531Cv8NPbMrQAk5EagpFGdsrlou3bieiY0ot6zOJ6JjijxCbVFwbF7kFTsVyboFDfJvqbwvRkYaivRan/KWSU+gQH+Oqd/1gEBttyNtdeS7I322Ji6p+7oyNYr8y3g4SeD9YX3BcKJdNDuXw3sHX/qriow051f6nlvhoVy1l9nsfaWPqVbclsphme/hLQ/6L59Wy7vy6ChtjLjXGzDPGzPv5a/+O7anr8GYXwOwVlrOPcXHWKBdZuRbHqaNwsKpHf8/ayp8Pv7uXO18tplOyi5SE4MsQNETreV7U8iQwDfvfespKWLtoOn2GTmyimAKTqeW4WGtrP142eD9M7VNrs/YvU0u9fU2vT/1gUGsb9y9zgLZO/biEJz4s5eUvSxne102XlCA+d9bnOVFX81rI80FqOmh+2BhzNvB7oKsx5sMqm2KA7LrqWWunAlMB7nrL47PnS0ERxEYa9j0lY6K866qXseWfAMvLRFaWWbTBsmiDd/3oAaZG3WCStxvi2lS+WuPaVM+cectY4quUia+lTHEJrNvm0LuTm8yc4JzkoudFpZiE9hTkVGbFCnIziYlPbtA+1i2dQUqn/kTHtmvq8AJK0dbtRKa1p3yeExEd27N3WxausFAi0yqzsxEdUyjeluWfIH+jUYPDGDEwHIBN28vKs4Tey6vxMS7yCqt/MiosskSGG1zGO3kjoU1lmdwC56D1g0He7sorLODNNNY8d1JexpaXgYLyMvvOD7uLYfkmh7QkFxszg3OiT26hJSGmMo8UH2PI2139f5pTaEmIqf4+kldocbsPXrcl0gQXr5nAA8DK8p/7Hv8A/J5m2LYLEmK8l2BdLujXybBma/UX+eqtlgFdvP/M1ETYW+p9UQNEec+ZxEZBnzTD8k3B+zkoI8uhXZwhIcbgdsGg7m6Wb6x+wlq+0cOhvbzjGDslG4pKvJdooyMgwjtmnRA39ExzsSMneF/kel5U6tB5ALuyNpK7cwueshKWz/2EHgPHNmgfK+Z9Qr/DJ/kowsCR9dF0Op57CgDxwwdRll/A3u07yJu7hOgeXYjskoYJDSX1zElkfjzdv8E20oxfSrj7lQLufqWARWtLGd7f+8Lv0sFN0V5L/u6az/XVW8oY0ss7TnF4/zAWr/OOX1y8rn71A93WnZbEOO9ENrcLBnZzsXJz9fPfys0OQ3p4z53pSYa9Jd5OYmgIhJWnXUJDoEeqi8wgPndu3u4hKd5FYqz3fWRor1CWrKueNFi6roxhfcv/7+3dFJdA/h5br7oSnA6aWbTWbgI2AUcaY1KAw8s3rbDW+v1ZYC18ucDhrNEuXOW3SNmZD0PKb3uycJ1l3a/Qo4Plz5O8t4v5+OfKF/KUES4iw8Bj4Yv5DsVBPJHNsfDBj6VcPCkMl4G5qzxk5liO6Oc9wc1e7mHlZoc+nSzXnh1OSRm88513kHJMlOHMsd56xsDidR5WbA7eE56eF5Vc7hAmnHkzbz16MdbxMPCoKSSl9mThjDcAGDLqbArzdvDy3VPYW1yIMS7mTX+Zi//9KeGRbSgtKWLDipkcd85tfm7Jbzf41QdIHD2MsHYJjN3wPWtuewwT6j0Nbp76JlmffU/S8aM5ZuVXeIqKWHzxDQBYj4elV9zGsE+ew7jdZLw0jcLlaw/0p4LCsvVl9O8ayi0Xx1BSCq99vqdi22WnRvP6F3vI223534xiLjwxipOOjmBLlodZS0oOWj+YOBY+mlXG+ceFYoxhwRoPWbmWYeXjD39e5bAqw6FXuourTgujtMzy3g/et782kXDOOG9H2mW8k2P2/2AaTBwL73xbzGW/i8IYw+xlJWzf5TBigLeNPy0pZdnGMvp1DeHm89tQWua9dc6B6rZ0reGm3MbWc9yNMeZ04H7gO7wjE0YC11hr3z1YXV9ehg42uTkt/55T9RWfEObvEAJCh2T3wQu1EskTevs7hIDx6d1z/B1CwIhv2wKnHTdSfv5ef4cQUB67MtbvPbW5q3KbrY9zeO94v7S3IXPabwIOt9ZmARhjkoCvgYN2FkVERERaIo1Z3K/svo5iuewG1hcRERGRINOQzOLnxpgvgDfKl88EPm36kERERESCQ8sfldmAzqK19hpjzBRgBN4xi1Otte/7LDIRERER8bsGfQ+PtXYaMM1HsYiIiIgEldYwZrE+N+UuoPabsBvAWmtjmzwqEREREQkI9bnPYkx9dmSMSbDW5hy8pIiIiEjL0Brus9iUs5m/acJ9iYiIiEgAaNCYxYNo+V1rERERkSpaw5jFpsws6ltaRERERFoY3VRbREREROqky9AiIiIijaQJLlUYY149yLpxTRKRiIiIiASMhmQW+1ddMMa4gaH7lq21u5oqKBEREZFg4LSCGRsHzSwaY64vvzH3QGNMfvmjAMgCPvB5hCIiIiLiN/XJLK611sYYY9621p7h84hEREREgoTGLHpdX/6zhy8DEREREZHAU5/MYrYx5lugqzHmw/03WmsnN31YIiIiIoGvNdyUuz6dxUnAocCrwAO+DUdEREREAslBO4vW2hJgtjHmKGvtjmaISURERCQo2FYwG7oht855wRhT5yHR5WgRERGRlqchncX1QHvgtfLls4GNwBdNHJOIiIhIUHBawWzohnQWh1hrR1VZ/sgYM8Nae0NTByUiIiIigaEhncUkY0w3a+16AGNMNyDJN2GJiIiIBD7Nhq7uSuA7Y8x6wAJdgUt9EZSIiIiIBIaGdBZjgUPwdhInA0cBO30RlIiIiEgwaA2zoevzDS77/Mtamw/EAOOBp4GnfBKViIiIiASEhnQWPeU/JwFPW2s/AMKaPiQRERERCRQNuQy91RjzDHAscI8xJpyGdTZFREREWhSrW+dUcwYwEbjfWptrjOkAXFOfikN6OY2JrUXKzIv0dwgB45T47/wdQkDYGN3f3yEEjGfvnuPvEALGCdcP93cIAePIBc/7O4SA8e9vBvo7BGmF6t1ZtNbuAd6rsvwr8KsvghIREREJBo4muIiIiIhIa9aQy9AiIiIiUkVruCm3MosiIiIiUidlFkVEREQaSTflFhEREZFWTZlFERERkUZyWsF9FpVZFBEREZE6KbMoIiIi0kgasygiIiIirZoyiyIiIiKNpPssioiIiEirpsyiiIiISCPpu6FFREREpFVTZ1FERERE6qTL0CIiIiKNpFvniIiIiEirpsyiiIiISCNZfd2fiIiIiLRmyiyKiIiINJJunSMiIiIirZoyiyIiIiKNpNnQIiIiItKqKbMoIiIi0kjKLIqIiIhI0DDGTDTGrDLGrDXGXFfL9nOMMYvLHzONMYMOtk9lFkVEREQaybGBc59FY4wbeAIYD2QAc40xH1prl1cptgEYba3NMcYcD0wFhh9ov8osioiIiLQMw4C11tr11toS4E3g5KoFrLUzrbU55YuzgbSD7VSZRREREZFGCrAxix2BLVWWMzhw1vAi4LOD7VSdRREREZEgYIy5FLi0yqqp1tqpVYvUUq3W7qwxZgzezuLRB/u7LaKzuOKXH3nv5f9gHQ9HjJ3CsSdfXG37vB8/5psPnwcgPDyK0y/+Fx079wFgz+583nrm3/yasRaAs/90O117DW7W+JvSuqUz+PrtO3Ech8FHn86REy+ttj17+zo+fukGMrcsY/TJf2f4hIsqtv389Uss+vEdMIakjr048by7CQkNb+4mNJlZvyzlwZfexnEcJo89mvNOmVht+/xlq7jmvidJTW4HwDHDhnDxaSfWq26w+WX+bF6a+giO4zB2womccvofqm2fO/sH3n7tOYwxuN1uzrvkcvr09455/uuFpxERGYXL5cLtdnP3w8/7owlN6vSxkfTvGkJJGbz62R62ZHlqlEmMc3HhiVFERRi2ZHp4+dM9eJz61w90A5+9i+QTjqEkK5sZQ06qtUy/h24keeJoPEXFLLroOvIXeoc9JU0YSb8Hb8S4XWx54R3W3fdsc4buE7MWLuHhF9/A41gmjxvJH393Qq3llq/dwCU33Mntf/8TY488DIC3PvmKD7+egbUw+dhRnHXi+OYMvcn17exmyugIXC7DrKUlfDWvpEaZKaPD6d81lJJSy2tfFpGxw6l33ZamOTOL5R3DqQcokgGkV1lOA7btX8gYMxB4DjjeWpt9sL8b9J1Fx/Hw7gt38OcbnyU+sT0P3nAmhwwdQ/u07hVlEpM68rebXyKqTRzLF/7AW1Nv5ao73wDg/Zf/Q5/BI7jgqocoKyulZG+Rv5rymzmOhy/fuI2zrnyR2IQUXrr7NHoOHEu71B4VZSKi4hl/1o2s+eWbanULcjKZN/0VLrnlU0LDInh/6hUsn/sJA486tbmb0SQ8jsN9L7zBYzdeSXJiAudffzcjDxtIt7TUauUG9+3Jg9f+tVF1g4Xj8fDCUw9y4x0PkZiYzPV/v5jDhh9NWqeuFWUGDBrKYcOPxhjDpg1refiem3no6f9WbL/5rkeJjYv3Q/RNr3/XEJISXNzyfAFdOrg5a3wk971eWKPcKaMimD5vL/NXlXLWsZEcNSCMHxaV1Lt+oMt4+T02Pvkag1+4p9btSRNHEd2jC9/1nUD88EEc8vgtzBxxBrhc9H/0ZuYcfwHFGZkcPftdMj+eTuGKdc3cgqbj8Tg88NzrPHLzP0hum8CF193OyMMG0zU9tUa5J197l+GDDqlYt25zBh9+PYPn/3MTISEh/P2OhxgxdCDpHVKauxlNwhg4fUwkT7y3m9xCyzVnR7NkfRnbdzkVZfp1CSE5wc1tLxXSpb2bM8dF8sCbu+tVV3xuLtDTGNMV2AqcBfy+agFjTCfgPeAP1trV9dlp0E9w2bR2Ce3ad6JdSjohIaEMOep4lsybXq1M195DiGoTB0CXngPJ25UJQPGeQtatmM8RY6YAEBISSlR0bPM2oAlt27CYhOTOJCSl4w4Jo+9hk1i9qHqnMDo2kdQuA3G5a35OcBwPZaXFOJ4ySkuKaROf3FyhN7nlazeQlpJMx5QkQkNCGH/UYcyYu8jndQPR2tUrSOmQRkr7joSEhnLUqGOZO/vHamUiIqMwxnv1Ym9xMbVfyWgZBvYIZc4yb7Zj468eIsMNsdE129srPYSFq0sBmLOshIE9QhtUP9Dt+nEepbvy6tyeMnkcW1/7HwC5cxYRGhdLePsk4ocNZM+6TRRtyMCWlrLtrU9IOWlcM0XtG8vXrietfflrPjSEY0cMY8bchTXKvfPZNxwzfCgJcTEV6zZm/Er/Xt2JCA8nxO1mSL/efD9nQXOG36Q6t3ezM88hO9/icWD+6lIGdK/+fjGgewg/ryh/DWz3EBkGsVGmXnXFt6y1ZcBfgS+AFcDb1tplxpg/GWP+VF7sZiAReNIY84sxZt7B9luv/6IxJhoostY6xpheQB/gM2ttaWMa05TydmWRkNi+Yjm+bQqb1i6ps/zsb9+j72Dv5fmdWRm0iU3gv0/dxLbNq0jv2o/fnXcd4RFRPo/bFwpzM4lNqDwWMQkpbNuwuF51YxJSGD7+Qp64fgwhoeF07TeCbv0OOowhYGXtyiUlMaFiOTkxgWVrN9Qot2T1es655naS2sZx+bmn0S09td51g8Wu7B0kJlV2/BPbJbF21fIa5X6e+T1vvPIMebk5XPfv+yo3GMOdN1+FAY49/mSOnXhyjbrBJK6Ni9yCykxHboFDfBsX+bsrLyVHRxqK9lqc8stLOYUO8TGuetdvCSJSUyjK2F6xXLx1OxEdU2pZn0n8sIH+CLHJ7NiVS3K7thXLyYkJLFtT/TWflZ3D9z8v4PF/X8OKpyq3de/UkWfeeJ+8gkLCw0KZtXAxfbp3aa7Qm1x8tCGn2vPb0qW9u5YylddecwstcW1Mveq2RE5gTXDBWvsp8Ol+656u8vvFwMX71zuQ+mYWZwARxpiOwDfABcBLB6pgjLnUGDPPGDPvs2nPNSSmBqr5X9qXIdnfmmU/M/vb9zjp91cB4HjKyNiwghHjz+Sa/7xLWHgk33wQvOOxbG3Hop4ZoqLdeaxZ9A2X3fkNf7v3B0r3FrF09gdNHWLzqeXFu/+R6N21Ex88cRev3/cvTp84hmvuf6redYNJbc8LanmNDDtqNA89/V+uvulu3nqtcgzabfc+xT2PvMD1tz7AFx+/x/Klv/gwWt+r7fSw/xGqdYS4rX/9lqC286i1to4DENxHwNYS//7NfPjFN/jLuafhdld/2+ySlsq5pxzP5bc9wN/veIgendNxu4K4g1Sf10ddJ8RW8tpojeqbHzbW2j3GmIuAx6y19xpjauboq6g6CPOzhaU+e77EtU0hJ7vyU27urkxiE5JqlNu2aRVvPnMz/3fd00THxAMQn9ieuLYpdOnp/VQ8aPgEvvnQlx1b34qJb09+TuWxKMjJrPel5I0rZxLXLo2oGO+n695DJpCxfiGHHBGcWaTkxHgys3MqlrOyc2iXEF+tTJuoyIrfRwwZwH3Pv0FufmG96gaTxMRksndkVSxn79xBQtt2dZbvd8hgnty+jfy8XGLj4mmb6C0bF5/AsCNHsW71cvodMtjXYTepUYPDGDHQO1lr0/ay8iyhNxMYH+Mir7D6mKrCIktkuMFlvFmDhDaVZXILnIPWbwmKtm4nMq09+14JER3bs3dbFq6wUCLTKq9gRHRMoXhbVu07CRLJiQlk7dxVsVzba37l+k3866FnAMgrKGTWgiW43S5GDzuUyeNGMnncSACeen0ayVWuTASb3EJLQkxlhzg+xpC3u/rzO6fQkhBT2TOMb2PIK7S43Qev2xLZALopt6/UN7NojDFHAucAn5SvC4iBCJ26H8LO7ZvJzsqgrKyUhTM/45ChY6qVydn5Ky88eCXn/uVuklO7VKyPjW9HQmJ7Mrd5LymsXjqblI7dCVapXQaQk7WR3J1b8JSVsGLeJ/QcNLZedWPbprJt/SJKS4qw1rJx5SzatQ/eY9G3exe2bM9iW9ZOSsvK+GrmPEYdVv0bjbJz8yoyCsvWbsBxHOJioutVN5h079WH7du2kLV9G2Wlpcyc8TWHDR9Rrcz2bRkVx2L92lWUlZYSExtHcXERRXv2AFBcXMTihXNJ79yt2dvwW834pYS7Xyng7lcKWLS2lOH9wwDo0sFN0V5L/u6an2dXbyljSC/vOMXh/cNYvM476mbxuvrVD3ZZH02n47mnABA/fBBl+QXs3b6DvLlLiO7RhcguaZjQUFLPnETmx9MPvLMA17dHV7b8msm2zB2Ulpbx9U8/M/LwwdXKvPfkPbz/1L28/9S9jDliKFdfci6jhx0KwK68fAC278jmuzkLGH/0Ab8MI6Bt3u4hKd5FYqzB7YKhvUJZsq6sWpml68oY1rf8NdDeTXEJ5O+x9aorwam+Hb4rgOuB98sHSnYDvvVdWPXndocw5YIbePqu/8NxPAwf8zs6pPfgp6/eAmDE+DP5YtpT7C7M450X7iiv4+Yfd70NwKkX3MBrj19LWVkpicnp/P5Pt/utLb+Vyx3C+LNu5s1HLsY6HgaOmEJSak8WfO+d+X3o6LMpzNvBS3dNYW9xIca4mPvNy1xyy6d07DqI3ocexwt3/A6XO4SU9L4MHnmmn1vUeCFuN1dfeBaX3+W9XcxJx4ygW3oq7331PQCnjh/N9NkLmPbV97hdbsLDQrnjikswxtRZN1i53SFc+KeruOvmq3Ach2PGTyK9cze++vR/AIw/4RTmzPyOGdM/x+0OISwsnCuvvRVjDHm5u7j/jhsA7wSoEaPHM3joEX5szW+3bH0Z/buGcsvFMZSUwmuf76nYdtmp0bz+xR7ydlv+N6OYC0+M4qSjI9iS5WHWkpKD1g8mg199gMTRwwhrl8DYDd+z5rbHMKHet4TNU98k67PvSTp+NMes/ApPURGLL/Y+D6zHw9IrbmPYJ89h3G4yXppG4fK1/mzKbxbidvOPi8/hyjsewnEcThx7NN3SO/LeF98BcOpxxxyw/g33PUleYaH33HHxOcS2ifZ90D7iWHjn22Iu+5130tvsZSVs3+UwYoD3g9NPS0pZtrGMfl1DuPn8NpSWeW+dc6C6LV2Qj8KoF1PbWI1qBbzfM/gfa+01jf0jvrwMHWwy80L9HULAOCX+O3+HEBA2Rvf3dwgB49kP9PrY54Trgzc71dSOXBC8Y8mb2r+/Ce7JRE3tsStj/X4N+JXvm29o5h9H+2cI/UEzi9ZajzFmaHMEIyIiIhJMAm02tC/U9zL0QmPMh8A7wO59K6217/kkKhEREREJCPXtLLYFsoGqsyUs3juAi4iIiLRKrWHMYr06i9baC3wdiIiIiIgEnnrdOscYk2aMed8Yk2WMyTTGTDPGpPk6OBEREZFAZm3zPfylvvdZfBH4EEgFOgIfla8TERERkRasvmMWk6y1VTuHLxljrvRBPCIiIiJBozXMhq5vZnGnMeZcY4y7/HEu3gkvIiIiItKC1bezeCFwBrC9/HFa+ToRERGRVqs1jFms72zozcBkH8ciIiIiIgFGs6FFREREpE6aDS0iIiLSSI7TfA9/qW9nMcla+6K1tqz88RKQ5MO4RERERCQAaDa0iIiISCO1hgkujZkN/SuaDS0iIiLSKmg2tIiIiEgj+TPj11wO2Fk0xjx6oO3W2subNhwRERERCSQHyyyeCtwIJAA5vg9HREREJHi0hq/7O1hnMR/4Du9tc8b4PBoRERERCSgH6yw+DXwOdAPmVVlvAFu+XkRERKRVss06aNE049+qdMDZ0NbaR621fYEXrLXdqjy6WmvVURQRERFp4eo7G/rPvg5EREREJNi0htnQ9b3PooiIiIi0QvXKLIqIiIhITf78zubmosyiiIiIiNRJmUURERGRRtKYRRERERFp1ZRZFBEREWmk1vANLsosioiIiEidmiWz+OPCVjBVqJ66dPJ3BAHE6nkBUOwJ83cIASO+bYS/QwgYRy543t8hBIxZh17k7xACxsKJU/0dQmC5crS/I2gVdBlaREREpJE0wUVEREREWjVlFkVEREQayTbrDBfTjH+rkjKLIiIiIlInZRZFREREGkm3zhERERGRVk2ZRREREZFG0mxoEREREWnVlFkUERERaSSnFQxaVGZRREREROqkzKKIiIhII2nMooiIiIi0asosioiIiDSSMosiIiIi0qopsygiIiLSSE4rSC0qsygiIiIidVJnUURERETqpMvQIiIiIo1kHX9H4HvKLIqIiIhInZRZFBEREWkkqwkuIiIiItKaKbMoIiIi0kiOxiyKiIiISGumzKKIiIhII2nMooiIiIi0asosioiIiDSS0/ITi8osioiIiEjdlFkUERERaSTbClKLyiyKiIiISJ1aRGaxZ0fDpCNCcBnDvNUeZiz21Cgzabib3uluSsss034oY1u295PA1aeHsbfUYq133MGTH5Y2d/hNasOyGUx/906s4zBgxOkMn3Bpte3Z29fx+Ws3kLVlGUef9HcOP/aiim3Fe/L54vWbyP51NWCYeO5dpHYb0swtaDqzflnGgy+/jeNYJo8dwXknH1dt+/xlq7nm/qdITW4HwDHDBnPxlEkA3P70K/y0YAkJsTG8cf/NzR57U1u8YBavP/sAjuMwevzJnHjaedW2L5jzPdNefwaXy+ByuTnn4qvo1W8w2TsymfrwLeTlZmOMYcxxv2PCSWf5qRVNQ+eLSrMWLuHhF9/A41gmjxvJH393Qq3llq/dwCU33Mntf/8TY488DIC3PvmKD7+egbUw+dhRnHXi+OYMvUkNfPYukk84hpKsbGYMOanWMv0eupHkiaPxFBWz6KLryF+4HICkCSPp9+CNGLeLLS+8w7r7nm3O0H3miku7c+TQRIr3erjrkVWsXldYo8ypk1I5Y3IaaamRTDrnJ/LyywCIjnJz8z/6kpIUjttteOO9LXz6TWZzN6HZtILJ0MHfWTQGTjoylBe/KCF/N/x5cigrNjvsyK387/VKc9EuzsWD75aQnmSYfFQIT39UeZJ//rNS9uz1R/RNy3E8fP32bZz+txeJiU/htXtPo/uAsbTr0KOiTER0PGNPv5G1i76pUX/6u3fStd9ITr7kUTxlJZSWFDdn+E3K4zjc98KbPHbj5SQnJnD+Df9h5NCBdEvrUK3c4D49ePDav9Sof+LoIzn9uGO49YmXmili33E8Hl555l7+eevjtE1M5parz2PIsJF07NStoky/gYczZNgojDFs3riGJ++9gf88+Q5ut5uzL7yCLt37ULRnN//+xx/pP2hYtbrBROeLSh6PwwPPvc4jN/+D5LYJXHjd7Yw8bDBd01NrlHvytXcZPuiQinXrNmfw4dczeP4/NxESEsLf73iIEUMHkt4hpbmb0SQyXn6PjU++xuAX7ql1e9LEUUT36MJ3fScQP3wQhzx+CzNHnAEuF/0fvZk5x19AcUYmR89+l8yPp1O4Yl0zt6BpHTG0LempUZz1fz/Tv3cMV/+5J5devbBGuSUr8pk5dxGP3TW42vpTJ3Vk4+bdXHv7UuJjQ/nv04fz5fdZlJW1gl5VC1Wvy9DGmPD6rPOHtHaGXfmWnALwOLB4vUPfTtWb1beTi4VrvdmDLTssEWEQE+mPaH1r+8bFJCR1Jr5dOu6QMPoMncS6xdU7hdExiXToPBCXu/rnhL1FhWSsncuAo04DwB0SRkRUbLPF3tSWr91IWvskOqYkERoSwvijDmPGvEX1rj+kb09io6N9GGHzWb9mGSnt00hu35GQ0FCGj5zAgp9nVCsTERmFMQaAkuIib68KiG/bji7d+wAQGRVNalpXcnbtaN4GNCGdLyotX7uetPbJ3tdIaAjHjhjGjLk1OwTvfPYNxwwfSkJcTMW6jRm/0r9XdyLCwwlxuxnSrzffz1nQnOE3qV0/zqN0V16d21Mmj2Pra/8DIHfOIkLjYglvn0T8sIHsWbeJog0Z2NJStr31CSknjWumqH1n5BGJfD59OwDLVhXQJjqExISwGuXWrC9ke1bNT07WWqKi3ABERrrJLyjD42m5HUXHsc328Jf6jlmcVc91zS422pC3u/IA5u+2xEWZ6mWi2K8MxJaXscAFx4Vy2eRQDu8d3EM4C3IziUloX7HcJj6Fgtz6pf7zdm4hqk1bPn/1el65+xS+eP1GSvbu8VWoPpe1K5eUxISK5eS2CezYlVuj3JI1Gzjnn3dw5d2PsX7LtmaMsPnkZO+gbbvKjE/bxGRysmt2+ObN+pbrLjudB2+/iov/dlON7Tsyt7Fp/Sq69+rv03h9SeeLSjt25ZLcrm3FcnJizddIVnYO3/+8gN9NOKba+u6dOvLL8tXkFRRSvHcvsxYuJjN7VzNE7R8RqSkUZWyvWC7eup2Ijim1rM8komNwZlerapcYTtbOyk5gVvZe2iXW7CzWZdon2+icFs3/Xj6Clx87jEeeXdsqLtW2ZAe8DG2MaQ90BCKNMUOAfWfVWCDqIHUvBS4FOP6PjzNk9MW/Pdra/k4t6/Z/TppaCu0rM/XjEgqKIDoCLpgYyo5cy8bMYH1W14zb1Nb4WjhOGZlbljPu9H/Roesgpr9zBz9/OZWjT7qyiWNsLgc/Fr27pvPB43cQFRHBTwuXcs0DTzPt4duaK8BmY2s9FjXLHXbkGA47cgwrly1g2uvPcO3tT1RsKy7aw2P3XMc5F19FZFQbX4brUzpfVKrtWyf2b/vDL77BX849Dbe7ese4S1oq555yPJff9gBREeH06JyO2+X2Zbh+Vdt51Fpbx5MlOJ8PVdX6rtGAZg0fksCaDYVcfuMiOnaI4KHbB7Lob/PZU1RzfLAEh4ONWTwOOB9IAx6ssr4AuOFAFa21U4GpADe+sNdnr5683Za46Mqndmy0IX+P3a8M5WVseRkoKC9TUOQts7sYlm9ySEtysTEzOJ/QMfHtKcip/JRbmJtJm7jketeNiW9Ph66DAOg1ZCJzvpzqkzibQ3LbBDKzcyqWs3bl0C4hrlqZNlGV1xZHDDmE+55/g9z8QuJjg7czVJu2icns2lmZYd6VnUV826Q6y/fpfyjPbr+VgvxcYmLjKSsr47H/XMtRo4/jsCPHNEfIPqPzRaXkxASydlZmA7Oyc2iXEF+tzMr1m/jXQ88AkFdQyKwFS3C7XYwediiTx41k8riRADz1+jSSq2TyW5qirduJTGvPvjNKRMf27N2WhSsslMi0yqs5ER1TKN6W5Z8gf6NTT0jlpOO8Y7pXrCkguV3lSLPkxHB27iqp975OOLY9r727BYCtvxbz6/ZiOqdFsWJNQdMGHSBa/df9WWtfttaOAc631o6p8phsrX2vmWI8oK07LYlxhoQ24HbBwG4uVm52qpVZudlhSA/vp970JMPeEu9JPzQEwsq7y6Eh0CPVRWaOs/+fCBrtOw8gJ2sjuTu34CkrYeX8T+g+YGy96kbHJRGT0J5dmesB2LRqFontu/syXJ/q270zW7ZnsS1rJ6VlZXw1cx6jhg6sViY7N6/iRb5s7UYca4mLaRnjFKvq2rMfmb9uYUfmVspKS5nzw5cMGTayWpnMX7dUHIuN61ZSVlZGm5g4rLU8/9jtpKZ3ZeLJ5/gj/Cal80Wlvj26suXXTLZl7qC0tIyvf/qZkYcPrlbmvSfv4f2n7uX9p+5lzBFDufqScxk97FAAduXlA7B9RzbfzVnA+KOHN3cTmk3WR9PpeO4pAMQPH0RZfgF7t+8gb+4Sont0IbJLGiY0lNQzJ5H58XT/BttI7326jQuumM8FV8znh9k7mTjW2wnu3zuGwj1lZOfUv7OYuWMvhw2KByAhPpROaVFsyyzyRdjSTA52Gfqq2n7fx1r74P7rmptj4aNZZZx/XCjGGBas8ZCVaxlWPp7o51UOqzIceqW7uOq0MErLLO/94J3e3yYSzhkXCoDLeAe7r9kavJ8QXO4Qxp1xM9OeuBjH8TDgyCm0S+3JLz+8AcDgkWezO28Hr947hZLiQoxxMf/bl7ngpk8Jj2zDuNP/xScvXY2nrJT4dulM/MPdfm5R44W43Vx9wVlcftdjOI7DSWOOolt6Ku995Z3Ycer4UUyfvZBpX8/A7XIRHhbKHZdfVHG56aZHn2fB8tXkFhRy4mXXc+lpJzJ57Ah/NqnR3O4Q/nDpNdx3y+U4jsOocSeR1qk70z+bBsDY46cwb+Z0fvz2U0JCQggNC+cv19yJMYbVy39h5nefkda5B/+60ttZPO3cyxh0WHAeC50vKoW43fzj4nO48o6HcByHE8ceTbf0jrz3xXcAnHrcMQesf8N9T5JXWOh9rV18DrFtgveD1uBXHyBx9DDC2iUwdsP3rLntMUyo9+1x89Q3yfrse5KOH80xK7/CU1TE4ou9F9asx8PSK25j2CfPYdxuMl6aRuHytf5sSpOYNW8XRx7WlremDqu4dc4+9/37EP7z2Gqyd5Vw2kkd+f2p6bRNCOPlRw9j1vxd3PPYal56axM3Xtmblx8bijGGp15aX3FbnZbIBu9nxnozB0qfGmP+faDK1tpb6/NHfHkZOth06RQQk8gDwumJwfkJvKmtjBzq7xACxkczI/wdQsD4x+Fz/R1CwJh16EUHL9RK3D0xeIcH+cKPH42u38B8H/rn00XN1se590+RfmnvATOL9e0MioiIiLRGTisYs3iwy9D/tNbea4x5jFrmQllrL/dZZCIiIiLidwebDb2i/Oc8XwciIiIiEmxaw2zog12G/qj858vNE46IiIiIBJJ6fTe0MeZbar8MXb/7soiIiIi0QP78Gr7mUq/OInB1ld8jgClAy50HLyIiIiJAPTuL1tr5+636yRjzvQ/iEREREQkarWDIYr0vQ7etsugChgLt6yguIiIiIi1EfS9Dz8c7ZtHgvfy8AdBdUkVERKRVsxqz6GWt7errQEREREQk8LjqU8gYE2GMucoY854xZpox5u/GGH0vl4iIiLRqjrXN9qgPY8xEY8wqY8xaY8x1tWw3xphHy7cvNsYcerB91quzCLwC9AceAx4H+gKv1rOuiIiIiPiYMcYNPAEcD/QDzjbG9Nuv2PFAz/LHpcBTB9tvfccs9rbWDqqy/O3/t3fncVJU997HP7+eGZYBgWERZVeiiApqcNdEExNjVDQXcEnMc0WTmNUlzzV5kpgoMU8SjdluzGLQGEjMjYgkxBDjRgJuIIZlWAVkUyCALA4zDMvM9O/+UWeYnmGWYpie7p75vl+vfnVV9anqX52uqj516pwqMyuOOa+IiIiIpN/ZwJvuvhbAzB4HrgaWp6S5GvidR4+emWtmPczsWHf/d0MLjVuzuNDMzq0eMbNzgFcOdw1ERERE2hJPequ9YugPvJ0yvjFMO9w0tTRas2hmS4h6QRcA/2lmb4XxwdQupYqIiIhIGpnZLUSXjqtNdPeJqUnqma1uKTNOmlqaugx9ZROfR99qVuTuu+KkFREREWkrWvPWOaFgOLGRJBuBgSnjA4DNzUhTS6OFRXff0NjnKWYCTfamEREREZG0eR04wcyOAzYB1wOfqJPmKeBLoT3jOUBJY+0VIX4Hl6bUV6UpIiIi0qZl0z253b3SzL4EPAvkAY+6+zIz+1z4/CHgaeBy4E2gHLipqeW2VGExi7JKREREpH1y96eJCoSp0x5KGXbgi4ezzJYqLIqIiIi0O+3hcX9xb53TFF2GFhEREWmDmqxZNLMEsNjdT20k2SUtF5KIiIhIbvCYj+HLZU3WLLp7Eig2s0GNpNnZolGJiIiISFaI22bxWGCZmc0D9lRPdPer0hKViIiISA5ItoM2i3ELi38GHgRUgygiIiLSjsQtLPYFbgcWAI8Cz3p7uEgvIiIi0oj2UByK1Rva3b8JnAD8BhgPrDaz75nZ0DTGJiIiIiIZFvs+i+7uZrYF2AJUAkXAk2b2vLt/NV0BioiIiGSr9nCfxViFRTO7DbgR2A48AnzF3SvCbXVWAyosioiIiLRBcWsWewNj3H1D6kR3T5rZlU3N3L17QXNia5OWrSjLdAhZY02n8zMdQlZIJHRP+2qlpfszHULWuGfmyEyHkDUWXjYx0yFkja8/c0umQ8gyKzMdQLsQq7Do7nc38tmKlgtHREREJHe0h8vQLfW4PxERERFpg2J3cBERERGR2pK6dY6IiIiItGeqWRQRERFpJrVZFBEREZF2TTWLIiIiIs2kx/2JiIiISLummkURERGRZkqqzaKIiIiItGeqWRQRERFpJvWGFhEREZF2TTWLIiIiIs2k3tAiIiIi0q6pZlFERESkmTyZzHQIaaeaRRERERFpkAqLIiIiItIgXYYWERERaSbdlFtERERE2jXVLIqIiIg0k26dIyIiIiLtmmoWRURERJpJj/sTERERkXZNNYsiIiIizaSaRRERERFp11SzKCIiItJMSdfj/kRERESkHVPNooiIiEgzqc2iiIiIiLRrbaJmceOql5g743skk0mGnTWO0y76TK3P3922lhenfYMdm5dz5qV3MOJ9N8eeN9ecNCiPMe/viBnMXV7BzPkVh6QZ8/4ODB+cT0Wl8z8v7GfjO0l6dDVu+HBHuhUmSLozZ1klLxYfOm8uOXFAgtHn5WMGr6+sYnZx1SFpRp+Xz7CBCSoqYersCjbvqDlDNINbP9aBknJn8rO5nRcn9DeuPDefRMJ4fWUVLy4+NC+uPDePYQPzOFDpTHux8mBefOXaDuyvcJIOyST88qnczovhg/MYe1EnEgljztIDPP+vA4ekGXtRR045roADFc5jz+1l4zvJ2PPmEuVFbbffMpTzRvVi3/4qvvffK1m1puyQNGOu6Me1Vw1gQL/OXHHDK5TsrgSgS2Eed//XcPr26UhenvHHP73N0zO3tvYqHLGRD3+Poy+/mAPbdvDiGaPrTXPyT+7i6MsuomrvPoo/9TV2L1wOQJ9L38fJP74Ly0vw9qNTWfPAw60ZesaoZjEHJJNVvPrUd7h0/ETG3vFX1hb/jV1b36yVpmNhd84bfVetQmLceXOJGYy7uCO/fmov9/2hnPeemE/fIquVZvjgPPr0SPDd35cz5R/7uebijkBUCPjLywf4/h/K+enUvVw4ouCQeXOJGVx9QT6/faaCnzx5gNOH5nF0j9rrM2xggt7djR8+cYA/vVzBxy4sqPX5Bafmse3d3D8ImMFV5xcw6bkKfjrtAKcdnzgkL04ckKBXtwQ/mnqA6S9XcvX5tc8jH3m6gp9Pr8j5gqIZXPOBzvxqejnf/V0Zo4YVcEzP2ofBk4fkc3RRHvdOKuPxmfu47pLOsefNJcqL2s4d1ZOB/Qq5/rPzeOAXq7jz8yfUm27Jit3c8a1i/r11X63pY67oz/q39jD+tvnc+vVivvSpoeTn594xdOPkPzHvyk83+Hmfy95Pl/cMYdbwS1ny+W9x6s8nRB8kEpzys7uZN/rTzB55Bf2uv5Kuw4e2TtCSdrm9dwPvbFxMt16D6NZzIHn5HTh+5OW8teIftdJ07tqLPgNGkEjkH/a8uWRw3wTb302yY7dTlYSFqyoZcXztdR5xfD6vr4jOhDdsTdK5o9Gt0Nhd7gdrDPZXwNZdSbp3zd3NY2AfY8duZ2dplBfFa6o4eXCdP8LBCRasjmrY3t7mdO4AR0X/hXTrAicNTPD6ykNr4HLNgJAXu0qhKgmL1yYZPujQvFj4ZsiLd5xOKXnRlgw+Jo/tJTX7yPxVFYwYWmcfGZrPvBVRLdn6LVV07gDdCi3WvLlEeVHb+87txTP/2ALAspWldO2ST6+iDoekW722jC3b9h8y3d0pLMwDoHPnPHaXVlJVlXsnmztf/hcVO0sa/LzvVZew6bHpALz7WjEF3bvR8Zg+9Dh7JOVrNrB33Ua8ooLNU/5G39GXtFLUmeXurfbKlNwtDQTlJdvo0v2Yg+OF3fuyZ3e8qv8jmTcbde9i7Cqr2ZjeLXO6d7V60iRT0iQPSdPzKGNAnwQbtuRuQalbF6MkJS9K9jjdutghad5tIM3ocwv4+7xK2sLz4bsXGiV7UtazvJ68KKRWmt3lHEzjwE2XFfDFqws4a1huHzJ6dDF2laZs/6VOjy6JetIcuh/FmTeXKC9q692rI9u21xQCt+3YT+9ehxYWGzLtb5sZPKAL0yefy+QHz+S/H36zTRw/6urUry97N245OL5v0xY69e9bz/StdOrfNxMhSho0eipoZkuI/ivq5e4jG5n3FuAWgDGf/RXnfPiW5sbYhEPDM+JW/R/JvFmontAPOVg1kaZDAdx0eSf+/NJ+9ufwFcc4v2JDaU4alKBsn7Npu3P8sTm8PTTmMLaLX884QGk5dOkEN19WwDslzvotOfovWN961k3S0E8eY96coryopd5VPYyVOueMIlavK+O2u4rpf2wnfvKdkRTfOp/yvbl70l0fq2ejcPf6N5a2WFpup5q6bnBleP9ieP99eL8BKG9sRnefCEwE+MG09LX+LOzelz0lNWcz5SVbKex2dNrnzUYlZU5RSi1hj67G7j1eT5oE60iGNImDaRIJuPmjnZi/spLFa3L7AFeyp3atavcu9eTFHqdHV2PDVq+V5tTj8jh5UB4nDcwjPw86doDrLi5gyqzcLD2XlDvdU2oSu4dmB6l27yGkiaZ3K4TSkKY07Ol79sHyDUkG9E6wPkdrnd8tc4qOqqkB63GUUbKn9g11d5U5RUfV3o9Kypy8vKbnzSXKCxhzeT9Gf+RYAFasLuXo3h0PfnZ0r45s3xm/087lHzqGx558G4BN/97Hv7fsY/CAQlasLm3ZoDNs76YtdB5wDLvCeKf+x7B/8zYSHQroPKDmSl2n/n3Zt3lbZoJsZclk7m37h6vR6wbuvsHdNwAXuPtX3X1JeH0N+EjrhNi4Pv1HsHv7Bkp3bqSq8gBrFz/NoOEfSPu82eitrUl690jQs5uRl4AzTsxn6braf+pL11Vy1vDoHGFw3wR7D/jBgsPHL+nI1l1JZi3KzUJRqo3vOL26GUVHRXlx2tA8lr9Ve4deviHJe0+I2hgNPNrYdwBK98Kzr1fy/T/u5/7H9/PHf1SwZnMyZwuKAJvecXp3M4q6Ql4CRh6fYEWdvFjxVpIz3hPyoo+xryLKi4L8qLYZouH39E+wdVfuHhjf2lJFnx4JeoV9ZNSJBSxZU1krzdI1lZw9PLr8OOSYPPYdgN3lHmveXKK8gD89vZmbbp/PTbfP56W527nsg1Fh55RhR1FWXsmOXfELi1vf2c+Zp/UAoKhHAYMGFLJ56950hJ1R2/76D/p/8mMA9DjnNCp3l7J/yzuUvL6ELu8ZQuchA7CCAvpddwVbZ+RuHwCpLW6L5C5mdqG7vwxgZucDXdIXVnyJvHzOu+qbPPPbT+Oe5MRRYyjqewIrXnscgOHnXE956Tv85RfXULG/DLMES1/5HWPvmEGHTl3rnTdXJR2mzd7P567qTCIBry2vYMvOJOefGv3Mry6tZPn6KoYPzuOb/1nIgQrnjzOjNjrHHZvgrJMK2Ly9iq9cH/VsmDHnACs25GYNUtLhqVcrufmjBSQM/rWyim27nHOGRwWi11ZUsfLtJCcNTPCV6zocvHVOW5R0eGpOJTddVoCZMX9VFdvedc4+KTpXnPdGkpVvJxk2IMF/XdOBikpn2kvRH3/XzvDJS6LSYiIBxWuSrN6Uu5eWkg5T/7mPL/xHIWbG3GUH2LIzyQUjonV8ZUkFy9ZXcvJx+dw9visVldHtYhqbN1cpL2qb86+dnHdmT6ZMPPvgrXOqPXDPqdz34Cp27DzAuNH9+cSYgfQs6sDkn53JnPk7uf/BVUyasoG77hjG5AdHYWb8atLag7fVySWn//5H9LrobDr0LuKD62az+t4HsYLoP+StiY+z7e+z6fPRi7j4jeep2ruXxZ/+BgBeVcXS2+/l7L89guXlsXHSNMqW5+7dRQ5He7h1jsXpXWNmo4BHge5E16lKgJvdfUGcL0nnZehcs3lzo1fv25VOnXK792RLSSTaaLvIZigtPbSXqcjCmQszHULW+Poz6Wr/n5uuqFiZ8QPolZ9Z3mplnBkPn5yR9Y31b+3u84HTzKwbUQGz4X71IiIiIu2Ee27XqscR614HZtbXzH4DTHH3EjM72cw+lebYRERERCTD4t4YaxLwLNAvjK8C7khDPCIiIiI5w5Peaq9MiVtY7O3uT0B0vxV3rwRys+eDiIiIiMQWt4fBHjPrRbgJm5mdS9TJRURERKTdag+9oeMWFv8v8BQw1MxeAfoA16QtKhERERHJCnELi8uAi4BhRE9FWkkbeK60iIiIyJFIqjf0QXPcvdLdl7n7UnevAOakMzARERERybxGaxbN7BigP9DZzM6g5lnr3YDCNMcmIiIiktXUZjF6/vN4YADw45TppcA30hSTiIiIiGSJRguL7j4ZmGxmY919WivFJCIiIpITPNn22yzGfdzfNDO7AjgF6JQy/d50BSYiIiIimRf3cX8PAdcBtxK1W7wGGJzGuEREREQkC8S9dc757j7SzBa7+7fN7EfAn9IZmIiIiEi2aw8dXOLeOmdveC83s35ABXBcekISERERkWwRt2Zxhpn1AB4AFhA99u/hdAUlIiIikgu8HdyUO24Hl++EwWlmNgPo5O56NrSIiIhIGxersGhmxcAUYIq7rwH2pzUqERERkRyQVJvFg64CKoEnzOx1M7vTzAalMS4RERERyQKxCovuvsHdf+Duo4BPACOBdWmNTERERCTLeTLZaq9MidvBBTMbAlxLdL/FKuCraYpJRERERLJE3DaLrwEFwFTgGndfm9aoRERERHJAe7jPYtyaxRvd/Y20RiIiIiIiWSduYfHfZvZj4P1hfDZwr26fIyIiIu1Ze7jPYtze0I8CpURtFq8FdgO/TVdQIiIiIpId4tYsDnX3sSnj3zazRWmIR0RERCRntIc2i7GfDW1mF1aPmNkF1DwvWkRERETaqLg1i58Dfmdm3QEDdgLj0xWUiIiISC7I5P0PW0vcZ0MXA6eZWbcwvjutUYmIiIhIVjD3pq+1m1lHYCwwhJQCprvfm7bI0sDMbnH3iZmOIxsoL2ooLyLKhxrKixrKixrKixrKi/YlbpvFvwBXEz0fek/KK9fckukAsojyoobyIqJ8qKG8qKG8qKG8qKG8aEfitlkc4O6XpTUSEREREck6cWsWXzWzEWmNRERERESyTtyaxQuB8Wa2DthP1CPa3X1k2iJLD7WvqKG8qKG8iCgfaigvaigvaigvaigv2pG4HVwG1zfd3Te0eEQiIiIikjViXYYOhcKBwAfDcHnceUVEREQkd8WtWbwHOBMY5u4nmlk/YKq7X5DuAEVEREQkc+LWDv4HcBXhdjnuvhk4Kl1BtTQzu9fMPpTpOA6HmQ0xs6WtPW82Odz1MLPx4USmeny9mfVOT3QiucnMbjOzFWa2ycx+nul4RCT7xS0sHvCoCtIBzKxL+kJqWWaW5+53u/sLmY4l08wsboemXDUe6NdUolRtMU/MrKOZvWBmi8zsOjP7Rox5ysJ7PzN7som0V5nZ11oq3kw6krxq5PMhZvaJlouyxX0BuBy4qyUW1hb3oWrNOfFO3T/MbIKZ3RmGa53MSvPypK1UhuSauIXFJ8zs10APM/sM8ALwcPrCiidsNG+Y2WQzW2xmT5pZYahRutvMXgauMbNJZjYuzHOWmb1qZsVmNs/MjjKzPDN7wMxeD8v5bIZXrVp+Pes2ysxmm9l8M3vWzI4FCNOLzWwO8MXqBYSdcaqZ/RV4zsx6mtn0sMy5ZjYypGto+oQQw3MhX8eY2Q/MbImZPWNmBSHdfWa2PMz/wzTnwd3ht1pqZhMtMo6oqcQfwh9/5zD/rWa2IMR7Uso6TTSz54ieeT7YzGaG75hpZoNCuoamTzKzX5nZP81srZldZGaPWlRbMymkyQvplobv/nIL5klTzgAK3P10d58CNFkAqubum919XBNpnnL3+440yCzR7LxqxBAgKwuLZvYQcDzwFFCUMr05+8CPzeyfwP1hH1gUXgvNLGeuPEG0v7bUshrZP8ZzmCezRyLOOoVjZyb7H4ynFfNEjoC7N/oiuk3OQODDwAPAD4EPNzVfa7yIDsoOXBDGHwXuBNYDX01JNwkYB3QA1gJnhendiG4fdAvwzTCtI/Av4LgsXLevAK8CfcK064BHw/Bi4KIw/ACwNAyPBzYCPcP4g8A9YfiDwKImpk8AXgYKgNOIOjd9NHz2Z+BjQE9gJTVtYHuk+fftmZLm98DoMDwLODPls/XArWH4C8AjKes0H+gcxv8K3BiGbwamNzF9EvA40b5xNbAbGEF08jUfOB0YBTyfEssR5QnQBfgbUAwsDb/9ZcAb4ff5GTADOBp4EygBFgFTgaow/IdGll+WkufV285rwCkpaWaF9RoP/DwlL35GtF2uBcaF6Qngl8CyENfT1Z+1wr7TWnllhH0NWAJcF6bPTVnml1tjnQ8zf9YDvev8js3ZB2YAeSnpqvfTrkB+ptezzjp/B7g9Zfy7wG3AP4H/AZY3MN+QsN1MJjrGPgkUpuZjGD4TmBWGU/N1AtExaxxQRnScXEQ49mRwnVaE/XMhMJjov+X1sI7frpPuYaL9+Dlqjpmnh+18MdH/QBEwHJhX53sWh+G7w/KXEt1yx+rLE6Ljy2yi4+izwLFh/lFE+/McUv7f9GrFfSjmRjk/04E2ENcQ4K2U8Q8C08NOPDhl+qSwYY4AXqlnOU8Cq8IGuwhYB1yahev2AlHBpDrOJWEH7l4n7UhqFxZ/m/LZQuD4lPG3w/wNTZ8A3BWmJQj32Qzj9wJ3EBW4i4HfAGOADmn+fccSFWSWAJuAr4XPZ3FoYbF/GD4HeCEMTyAUjMP4dqLaJYgKxdubmD4JuCEMHw+sTlnW74gK0EXAGqJC+GVA4gjzYizwcMp49/AbnUB04H0CmBE+u7h6OIyXxVh+fYXFL1Pzx3EssCplm0otLE4N28bJwJth+jiiAmICOAbYResVFlsrr8YCzwN5QF/grZBPtZaZbS/qLyw2Zx+4MWWZXyPaJ28jeuJXxtezzjoPARaE4UTYN8cStcNvsGKABk5YU/MxDDdaWAzDs0g5PmV4nZLAuWH8UmoKcAmik4D3h3SVwOkh3RPAJ8NwauXEvcBPw/Aiwv8I8P+oqYRp8gQ/bF+HVRmiV+u94lY/zzWzs2KmbW3ewHh9z662etJXT7/Vo0tRp7v7ce7+XEsG2Ux1Yy0FlqXEOcLdL6Xh9aqWmhfWwPc0NB2iAiLungQqPOyxRAecfHevBM4GphEVlJ5pJJbDVd/v+0uigscIorPeTo3Mvz+8V1H7JvSNPdu8obxMnV693GTKcPV4vrvvIqqJnUXULOCRRr4vjiXAh8zsfjN7H3AcsM7dV4ff47EjXH59ngCuCcPXEhUK6zPd3ZPuvpyo0ATRjfynhulbiGo7Wktr5dWFwB/dvcrdtxLViGTrcfJwxdkHDu5DHl12/TRR7dDc6iYf2cLd1wM7zOwMosLRQmAHUU3YuiZmf9vdXwnDjxH97hl3hOu0wd3nhuFLU+ZfAJxEdGIF0X6zKAzPB4aYWXeiKyWzw/TJRIVLiI4Z14bh64ApYfgDZvaamS0hOuk/pZ6YhgGnAs+b2SLgm8CAer7v902sm6RB3MLiB4A5ZrYmtF1ZYmaL0xnYYRhkZueF4Y8TXWZqyBtAv+qCr0XtFfOJqrs/n9L+7kTLjk48dddtLtCnepqZFZjZKe7+LlBiZtUHsRsaWeaL1Z+b2cVENQW7G5neJDPrCnR396eJahpPj7V28TT0+24P35vavq6U5vXSfxW4PgzfkPIdDU1vkkW9sBPuPg34FvDeZsR1kLuvIroUswT4PtHdCRo7QThi7r6J6M9oJNGB//EGkqYWlq3Oe6trxbzK2DqmwRHtA2Y21N2XuPv9RM14sqqwGDxCVOt3E1ENITR+0litoQqJSmr+Qxs7YU2n5q5T3QqE76dUQrzH3X8TPkvdt+uecNdnCnCtmZ1I9JS31WbWiXgn+EbzKkOkFcQtLH4UGEp0RjAauDK8A2BmRQ3M1xpWADeGwmtP4FcNJXT3A0R/eg+aWTHRJaRORDvccmCBRb2sfk38RyGmU911e5CocHR/iH8RcH5IexPwC4s6uOxtZJkTgDPDMu8DbmxiehxHATPCvLOJLl+2lPp+34eJCgLTidrBVJsEPFSng0sctwE3he/4P8DtTUyPoz8wK5whTwK+fhjzHsKiHoPl7v4YUbvh84HjzGxoSPLxRmavqD4RaobHga8SnQwsOYz5XgbGmlnCzPoSXZptFa2YVy8C14XOTH2Ialfm0fyTlkw60n3gjtCZq5jo+PP3dAfcDH8mahJyFlEFQVwNnbCuJzopgejyb1PSsV00d51SPQvcHE6+MbP+ZnZ0Q4ndvQTYFWrtIdouZofP1hAVKr9FTa1idcGwqRP8lRx5ZYikS0tcyya0m2jtFyntq/TSqy2/gI8QtdtZRFRAPpPanTbuo+F2ePcTFboPq4NLGO9LVINyT8q08dRusziunuUkgIeITsKmExUeWqVjXCvmVUMdXAqAmUTteLOug0t7foVt8r76fvsG0g8J2/BDYZuaRk0Hl/cRtXV/ieikZFaYnrp/TKCmzeJYWrCDyxGu09I6024P2/ASok4kQ+s5FtwJTAjDp1PTwWU6UFQnnQNDUqb9f6LOZC8Av01ZTq08Cct9Mew7y4DPhHSpHVwm1I1fr/S/Yj3BpSlmttDdzzjiBR3+9w4h2jFObe3vFskmoenAne5+ZYZDOcjMurp7mZn1Iqpxu8Cj9ouZjutisiyvJP3CLWIWANe4++pMx9MS2uI6SXZqqfsrZaQ9gbuvV0FRJGvNCJfhXwK+kw0FRWmfzOxkopqtmW2lUNUW10myV0vVLC5w9yNqwC8i6RVq+GbW89El7r6jtePJZsqr9qUt/t5tcZ0kc3L6MrSIiIiIpFeTl6FDb8amnsN4SQvFIyIiIiJZpMnCokc3Yi6ufiZoA2l2tmhUIiIiIpIV4t5L8FhgmZnNo/Zd+69KS1QiIiIikhXiFhb/THRDaNUgioiIiLQjcQuLfYlu2rmA6JFCz3pL9IwRERERkawWuze0mRnRw8ZvInoiwhPAbzx6vI+IiIiItEGxb8odahK3hFclUAQ8aWY/SFNsIiIiIpJhsWoWzew24EZgO/AIMN3dK8Kjhla7+9D0hikiIiIimRC3zWJvYIy7b0id6O5JM9PzVUVERETaqBZ5gouIiIiItE2x2yyKiIiISPujwqKIiIiINEiFRRERERFpkAqLIiIiItKg/wXrlNigOUgomAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 864x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Heatmap of Feature Correlation\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "sns.heatmap(dataset.corr(), annot=True, fmt=\".2f\", cmap='coolwarm')\n",
+    "plt.title('Correlation Heatmap')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A heatmap showing the correlation coefficients between different features in the dataset.\n",
+    "\n",
+    "Strong Correlations: High positive values (closer to +1.0) indicate a strong positive relationship between features, useful for predicting one from another.\n",
+    "\n",
+    "Negative Correlations: Values closer to -1.0 show a strong inverse relationship.\n",
+    "\n",
+    "Multicollinearity Concerns: Features that are highly correlated with others (except price) may lead to multicollinearity in regression models, potentially necessitating removal or combining of features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -40,7 +1782,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,