diff --git a/student.ipynb b/student.ipynb
index d3bb34af..f8cc5c88 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -6,27 +6,5209 @@
    "source": [
     "## Final Project Submission\n",
     "\n",
-    "Please fill out:\n",
-    "* Student name: \n",
-    "* Student pace: self paced / part time / full time\n",
-    "* Scheduled project review date/time: \n",
-    "* Instructor name: \n",
-    "* Blog post URL:\n"
+    "* Student names:\n",
+    "\n",
+    "Jeremiah Waiguru\n",
+    "\n",
+    "Olive Muloma\n",
+    "\n",
+    "Troye Gilbert\n",
+    "\n",
+    "Josephine Maro\n",
+    "\n",
+    "\n",
+    "* Student pace: FULL TIME HYBRID\n",
+    "* Scheduled project review date/time: N/A\n",
+    "* Instructor name: MARYANN MWIKALI\n",
+    "* Blog post URL: N/A\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data\n",
+    "We have been provided with a dataset with house sale prices in King County, Washington State, USA from 2014 to 2015 to use for this project.\n",
+    "\n",
+    "A dataset has been provided and can be found in the kc_house_data.csv file in this repository.\n",
+    "\n",
+    "The column names and descriptions as provided can be found in the column_names.md file in this repository.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import the necessary libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib as plt\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import statsmodels.formula.api as sfm\n",
+    "import statsmodels.api as sm\n",
+    "import scipy.stats as stats\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### loading dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>10/13/2014</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>2/25/2015</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>2/18/2015</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+       "id                                                                             \n",
+       "7129300520  10/13/2014  221900.0         3       1.00         1180      5650   \n",
+       "6414100192   12/9/2014  538000.0         3       2.25         2570      7242   \n",
+       "5631500400   2/25/2015  180000.0         2       1.00          770     10000   \n",
+       "2487200875   12/9/2014  604000.0         4       3.00         1960      5000   \n",
+       "1954400510   2/18/2015  510000.0         3       2.00         1680      8080   \n",
+       "\n",
+       "            floors waterfront  view  condition          grade  sqft_above  \\\n",
+       "id                                                                          \n",
+       "7129300520     1.0        NaN  NONE    Average      7 Average        1180   \n",
+       "6414100192     2.0         NO  NONE    Average      7 Average        2170   \n",
+       "5631500400     1.0         NO  NONE    Average  6 Low Average         770   \n",
+       "2487200875     1.0         NO  NONE  Very Good      7 Average        1050   \n",
+       "1954400510     1.0         NO  NONE    Average         8 Good        1680   \n",
+       "\n",
+       "           sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
+       "id                                                                            \n",
+       "7129300520           0.0      1955           0.0    98178  47.5112 -122.257   \n",
+       "6414100192         400.0      1951        1991.0    98125  47.7210 -122.319   \n",
+       "5631500400           0.0      1933           NaN    98028  47.7379 -122.233   \n",
+       "2487200875         910.0      1965           0.0    98136  47.5208 -122.393   \n",
+       "1954400510           0.0      1987           0.0    98074  47.6168 -122.045   \n",
+       "\n",
+       "            sqft_living15  sqft_lot15  \n",
+       "id                                     \n",
+       "7129300520           1340        5650  \n",
+       "6414100192           1690        7639  \n",
+       "5631500400           2720        8062  \n",
+       "2487200875           1360        5000  \n",
+       "1954400510           1800        7503  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# loading the data and previewing the dataframe\n",
+    "df = pd.read_csv('data/kc_house_data.csv', index_col=0)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preparation\n",
+    "In this section, we shall be preparing the data for further processing and modelling\n",
+    "\n",
+    "### Investigate data types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(21597, 20)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# shape of our data\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2.159700e+04</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>2.159700e+04</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>17755.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.402966e+05</td>\n",
+       "      <td>3.373200</td>\n",
+       "      <td>2.115826</td>\n",
+       "      <td>2080.321850</td>\n",
+       "      <td>1.509941e+04</td>\n",
+       "      <td>1.494096</td>\n",
+       "      <td>1788.596842</td>\n",
+       "      <td>1970.999676</td>\n",
+       "      <td>83.636778</td>\n",
+       "      <td>98077.951845</td>\n",
+       "      <td>47.560093</td>\n",
+       "      <td>-122.213982</td>\n",
+       "      <td>1986.620318</td>\n",
+       "      <td>12758.283512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.673681e+05</td>\n",
+       "      <td>0.926299</td>\n",
+       "      <td>0.768984</td>\n",
+       "      <td>918.106125</td>\n",
+       "      <td>4.141264e+04</td>\n",
+       "      <td>0.539683</td>\n",
+       "      <td>827.759761</td>\n",
+       "      <td>29.375234</td>\n",
+       "      <td>399.946414</td>\n",
+       "      <td>53.513072</td>\n",
+       "      <td>0.138552</td>\n",
+       "      <td>0.140724</td>\n",
+       "      <td>685.230472</td>\n",
+       "      <td>27274.441950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>7.800000e+04</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>5.200000e+02</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>1900.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98001.000000</td>\n",
+       "      <td>47.155900</td>\n",
+       "      <td>-122.519000</td>\n",
+       "      <td>399.000000</td>\n",
+       "      <td>651.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.220000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.750000</td>\n",
+       "      <td>1430.000000</td>\n",
+       "      <td>5.040000e+03</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1190.000000</td>\n",
+       "      <td>1951.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98033.000000</td>\n",
+       "      <td>47.471100</td>\n",
+       "      <td>-122.328000</td>\n",
+       "      <td>1490.000000</td>\n",
+       "      <td>5100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>4.500000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.250000</td>\n",
+       "      <td>1910.000000</td>\n",
+       "      <td>7.618000e+03</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>1560.000000</td>\n",
+       "      <td>1975.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98065.000000</td>\n",
+       "      <td>47.571800</td>\n",
+       "      <td>-122.231000</td>\n",
+       "      <td>1840.000000</td>\n",
+       "      <td>7620.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.450000e+05</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>2550.000000</td>\n",
+       "      <td>1.068500e+04</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2210.000000</td>\n",
+       "      <td>1997.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98118.000000</td>\n",
+       "      <td>47.678000</td>\n",
+       "      <td>-122.125000</td>\n",
+       "      <td>2360.000000</td>\n",
+       "      <td>10083.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.700000e+06</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>13540.000000</td>\n",
+       "      <td>1.651359e+06</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>9410.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>98199.000000</td>\n",
+       "      <td>47.777600</td>\n",
+       "      <td>-121.315000</td>\n",
+       "      <td>6210.000000</td>\n",
+       "      <td>871200.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\n",
+       "count  2.159700e+04  21597.000000  21597.000000  21597.000000  2.159700e+04   \n",
+       "mean   5.402966e+05      3.373200      2.115826   2080.321850  1.509941e+04   \n",
+       "std    3.673681e+05      0.926299      0.768984    918.106125  4.141264e+04   \n",
+       "min    7.800000e+04      1.000000      0.500000    370.000000  5.200000e+02   \n",
+       "25%    3.220000e+05      3.000000      1.750000   1430.000000  5.040000e+03   \n",
+       "50%    4.500000e+05      3.000000      2.250000   1910.000000  7.618000e+03   \n",
+       "75%    6.450000e+05      4.000000      2.500000   2550.000000  1.068500e+04   \n",
+       "max    7.700000e+06     33.000000      8.000000  13540.000000  1.651359e+06   \n",
+       "\n",
+       "             floors    sqft_above      yr_built  yr_renovated       zipcode  \\\n",
+       "count  21597.000000  21597.000000  21597.000000  17755.000000  21597.000000   \n",
+       "mean       1.494096   1788.596842   1970.999676     83.636778  98077.951845   \n",
+       "std        0.539683    827.759761     29.375234    399.946414     53.513072   \n",
+       "min        1.000000    370.000000   1900.000000      0.000000  98001.000000   \n",
+       "25%        1.000000   1190.000000   1951.000000      0.000000  98033.000000   \n",
+       "50%        1.500000   1560.000000   1975.000000      0.000000  98065.000000   \n",
+       "75%        2.000000   2210.000000   1997.000000      0.000000  98118.000000   \n",
+       "max        3.500000   9410.000000   2015.000000   2015.000000  98199.000000   \n",
+       "\n",
+       "                lat          long  sqft_living15     sqft_lot15  \n",
+       "count  21597.000000  21597.000000   21597.000000   21597.000000  \n",
+       "mean      47.560093   -122.213982    1986.620318   12758.283512  \n",
+       "std        0.138552      0.140724     685.230472   27274.441950  \n",
+       "min       47.155900   -122.519000     399.000000     651.000000  \n",
+       "25%       47.471100   -122.328000    1490.000000    5100.000000  \n",
+       "50%       47.571800   -122.231000    1840.000000    7620.000000  \n",
+       "75%       47.678000   -122.125000    2360.000000   10083.000000  \n",
+       "max       47.777600   -121.315000    6210.000000  871200.000000  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Describing the data\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "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": [
+    "# summary of the data\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataset contains 21,597 entries and 20 columns\n",
+    "\n",
+    "Some columns like 'waterfront', 'view', 'yr_renovated' have missing values\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### loading the column.md dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('data/column_names.md', 'r') as file:\n",
+    "    md_lines = file.readlines()\n",
+    "\n",
+    "df1 = pd.DataFrame({'Text': md_lines})\n",
+    "\n",
+    "pd.set_option('display.max_colwidth',None)\n",
+    "\n",
+    "# df1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### cleaning the column_md dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1[['Column name', 'Descriptions']] = df1['Text'].str.split('-', n=1,expand=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### drop the original 'text' column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1.drop(columns=['Text'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### remove rows where 'descriptions' columns contains 'None'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Column name</th>\n",
+       "      <th>Descriptions</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>* `id`</td>\n",
+       "      <td>Unique identifier for a house\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>* `date`</td>\n",
+       "      <td>Date house was sold\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>* `price`</td>\n",
+       "      <td>Sale price (prediction target)\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>* `bedrooms`</td>\n",
+       "      <td>Number of bedrooms\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>* `bathrooms`</td>\n",
+       "      <td>Number of bathrooms\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>* `sqft_living`</td>\n",
+       "      <td>Square footage of living space in the home\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>* `sqft_lot`</td>\n",
+       "      <td>Square footage of the lot\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>* `floors`</td>\n",
+       "      <td>Number of floors (levels) in house\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>* `waterfront`</td>\n",
+       "      <td>Whether the house is on a waterfront\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>* `view`</td>\n",
+       "      <td>Quality of view from house\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>* `condition`</td>\n",
+       "      <td>How good the overall condition of the house is. Related to maintenance of house.\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>* `grade`</td>\n",
+       "      <td>Overall grade of the house. Related to the construction and design of the house.\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>* `sqft_above`</td>\n",
+       "      <td>Square footage of house apart from basement\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>* `sqft_basement`</td>\n",
+       "      <td>Square footage of the basement\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>* `yr_built`</td>\n",
+       "      <td>Year when house was built\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>* `yr_renovated`</td>\n",
+       "      <td>Year when house was renovated\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>* `zipcode`</td>\n",
+       "      <td>ZIP Code used by the United States Postal Service\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>* `lat`</td>\n",
+       "      <td>Latitude coordinate\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>* `long`</td>\n",
+       "      <td>Longitude coordinate\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>* `sqft_living15`</td>\n",
+       "      <td>The square footage of interior housing living space for the nearest 15 neighbors\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>* `sqft_lot15`</td>\n",
+       "      <td>The square footage of the land lots of the nearest 15 neighbors\\n</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Column name  \\\n",
+       "1              * `id`    \n",
+       "2            * `date`    \n",
+       "3           * `price`    \n",
+       "4        * `bedrooms`    \n",
+       "5       * `bathrooms`    \n",
+       "6     * `sqft_living`    \n",
+       "7        * `sqft_lot`    \n",
+       "8          * `floors`    \n",
+       "9      * `waterfront`    \n",
+       "11           * `view`    \n",
+       "13      * `condition`    \n",
+       "15          * `grade`    \n",
+       "17     * `sqft_above`    \n",
+       "18  * `sqft_basement`    \n",
+       "19       * `yr_built`    \n",
+       "20   * `yr_renovated`    \n",
+       "21        * `zipcode`    \n",
+       "22            * `lat`    \n",
+       "23           * `long`    \n",
+       "24  * `sqft_living15`    \n",
+       "25     * `sqft_lot15`    \n",
+       "\n",
+       "                                                                           Descriptions  \n",
+       "1                                                       Unique identifier for a house\\n  \n",
+       "2                                                                 Date house was sold\\n  \n",
+       "3                                                      Sale price (prediction target)\\n  \n",
+       "4                                                                  Number of bedrooms\\n  \n",
+       "5                                                                 Number of bathrooms\\n  \n",
+       "6                                          Square footage of living space in the home\\n  \n",
+       "7                                                           Square footage of the lot\\n  \n",
+       "8                                                  Number of floors (levels) in house\\n  \n",
+       "9                                                Whether the house is on a waterfront\\n  \n",
+       "11                                                         Quality of view from house\\n  \n",
+       "13   How good the overall condition of the house is. Related to maintenance of house.\\n  \n",
+       "15   Overall grade of the house. Related to the construction and design of the house.\\n  \n",
+       "17                                        Square footage of house apart from basement\\n  \n",
+       "18                                                     Square footage of the basement\\n  \n",
+       "19                                                          Year when house was built\\n  \n",
+       "20                                                      Year when house was renovated\\n  \n",
+       "21                                  ZIP Code used by the United States Postal Service\\n  \n",
+       "22                                                                Latitude coordinate\\n  \n",
+       "23                                                               Longitude coordinate\\n  \n",
+       "24   The square footage of interior housing living space for the nearest 15 neighbors\\n  \n",
+       "25                    The square footage of the land lots of the nearest 15 neighbors\\n  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = df1[df1['Descriptions'].notna()]\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot',\n",
+       "       'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above',\n",
+       "       'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',\n",
+       "       'sqft_living15', 'sqft_lot15'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking column names\n",
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date                0\n",
+       "price               0\n",
+       "bedrooms            0\n",
+       "bathrooms           0\n",
+       "sqft_living         0\n",
+       "sqft_lot            0\n",
+       "floors              0\n",
+       "waterfront       2376\n",
+       "view               63\n",
+       "condition           0\n",
+       "grade               0\n",
+       "sqft_above          0\n",
+       "sqft_basement       0\n",
+       "yr_built            0\n",
+       "yr_renovated     3842\n",
+       "zipcode             0\n",
+       "lat                 0\n",
+       "long                0\n",
+       "sqft_living15       0\n",
+       "sqft_lot15          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# function to check null values\n",
+    "def check_null(df):\n",
+    "    return df.isna().sum()\n",
+    "\n",
+    "# checking for null values in the data\n",
+    "check_null(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are missing values in three columns.\n",
+    "\n",
+    "Depending on the ratio of missing values, we will decide on what approach to take in dealing with them"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dealing with missing values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There is 11.00152798999861 percent of values missing in waterfront.\n",
+      "There is 0.29170718155299347 percent of values missing in view.\n",
+      "There is 17.78950780200954 percent of values missing in yr_renovated.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# function to calculate percentage of null values\n",
+    "def miss_percent(df,col):\n",
+    "    miss = ((df[col].isna().sum()) / len(df[col])) * 100\n",
+    "    return print(f'There is {miss} percent of values missing in {col}.')\n",
+    "\n",
+    "# checking percentage of missing values \n",
+    "miss_percent(df,'waterfront')\n",
+    "miss_percent(df, 'view')\n",
+    "miss_percent(df, 'yr_renovated')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The threshold on how to deal with missing values commonly used is 50% and also depends on the specific column. The percentages of missing values are very low for the specific columns so we can replace.\n",
+    "\n",
+    "Checking the year renovated column we may assume the missing value is because the house was never renovated, maybe the house did not have a view or a waterfront also for the other two columns hence we can Fill them with zeros.\n",
+    "\n",
+    "Since the missing values in the 3 columns are categorical and are a small percentage of the columns, replacing them with mode won`t skew the data nor give false conclusions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There is 11.00% of values missing in waterfront.\n",
+      "There is 0.29% of values missing in view.\n",
+      "There is 17.79% of values missing in yr_renovated.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def miss_percent(df, col, fill_value=None):\n",
+    "    miss = ((df[col].isna().sum()) / len(df[col])) * 100\n",
+    "    if fill_value is not None:\n",
+    "        df[col].fillna(fill_value, inplace=True)\n",
+    "    return miss\n",
+    "\n",
+    "# checking percentage of missing values and filling missing values with the mode\n",
+    "fill_values = {'waterfront': df['waterfront'].mode()[0], \n",
+    "               'view': df['view'].mode()[0], \n",
+    "               'yr_renovated': df['yr_renovated'].mode()[0]}\n",
+    "\n",
+    "for col in fill_values:\n",
+    "    missing_percent = miss_percent(df, col, fill_value=fill_values[col])\n",
+    "    print(f'There is {missing_percent:.2f}% of values missing in {col}.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### check duplicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duplicates = df.duplicated().sum()\n",
+    "duplicates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### EDA 1\n",
+    "\n",
+    "Exploratory data analysis, including visualizations like scatter plots, bar plots, and heatmaps, is essential before creating regression models. These analyses help understand variable relationships, identify influential predictors, validate model assumptions, and guide feature selection. Visual exploration ensures that subsequent regression models are well-informed, optimized, and interpretable. It also aids in interpreting results and effectively communicating insights, enhancing the overall quality and utility of regression analysis. Conducting thorough exploratory analysis before modeling is crucial for building reliable and actionable regression models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***1. Heatmap***\n",
+    "\n",
+    "The heatmap of correlations between price and features (bedrooms, bathrooms, sqft_living, sqft_lot) is essential for both linear and multilinear regression. It identifies influential predictors based on their relationships with the target variable (price). This helps prioritize predictors in linear models and detect multicollinearity in multilinear models, ensuring stable and interpretable models. Overall, the heatmap guides feature selection and model interpretation in regression analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 720x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.heatmap(df[['price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot']].corr(), annot=True, cmap='coolwarm')\n",
+    "plt.title('Correlation Heatmap')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***2. Bar Graph***\n",
+    "\n",
+    "The bar plot of price by condition is essential for developing regression models. It identifies influential condition categories for predictor selection and aids in understanding price variations. This visualization guides preprocessing of categorical variables and validates predictor-target relationships. Overall, it informs feature selection, interpretation, and validation in regression modeling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(x='condition', y='price', data=df, color='blue')\n",
+    "plt.title('Price Distribution by Condition')\n",
+    "plt.xlabel('Condition')\n",
+    "plt.ylabel('Price')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***3. Scatter plot***\n",
+    "\n",
+    "The scatter plots of price against square footage of living space (`sqft_living`) and lot size (`sqft_lot`) provide insights for linear and multilinear regression models. They show how price relates to these predictors, helping assess linearity and identify outliers. Clear trends in these plots guide decisions on model complexity and feature engineering, essential for accurate regression analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.scatterplot(x='sqft_living', y='price', data=df)\n",
+    "plt.title('Price vs Sqft Living')\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.scatterplot(x='sqft_lot', y='price', data=df)\n",
+    "plt.title('Price vs Sqft Lot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PRE-PROCESSING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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     21597 non-null  object \n",
+      " 8   view           21597 non-null  object \n",
+      " 9   condition      21597 non-null  object \n",
+      " 10  grade          21597 non-null  object \n",
+      " 11  sqft_above     21597 non-null  int64  \n",
+      " 12  sqft_basement  21597 non-null  object \n",
+      " 13  yr_built       21597 non-null  int64  \n",
+      " 14  yr_renovated   21597 non-null  float64\n",
+      " 15  zipcode        21597 non-null  int64  \n",
+      " 16  lat            21597 non-null  float64\n",
+      " 17  long           21597 non-null  float64\n",
+      " 18  sqft_living15  21597 non-null  int64  \n",
+      " 19  sqft_lot15     21597 non-null  int64  \n",
+      "dtypes: float64(6), int64(8), object(6)\n",
+      "memory usage: 4.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>2014-10-13</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>2015-02-25</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>2015-02-18</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263000018</th>\n",
+       "      <td>2014-05-21</td>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6600060120</th>\n",
+       "      <td>2015-02-23</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300141</th>\n",
+       "      <td>2014-06-23</td>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291310100</th>\n",
+       "      <td>2015-01-16</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300157</th>\n",
+       "      <td>2014-10-15</td>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+       "id                                                                            \n",
+       "7129300520 2014-10-13  221900.0         3       1.00         1180      5650   \n",
+       "6414100192 2014-12-09  538000.0         3       2.25         2570      7242   \n",
+       "5631500400 2015-02-25  180000.0         2       1.00          770     10000   \n",
+       "2487200875 2014-12-09  604000.0         4       3.00         1960      5000   \n",
+       "1954400510 2015-02-18  510000.0         3       2.00         1680      8080   \n",
+       "...               ...       ...       ...        ...          ...       ...   \n",
+       "263000018  2014-05-21  360000.0         3       2.50         1530      1131   \n",
+       "6600060120 2015-02-23  400000.0         4       2.50         2310      5813   \n",
+       "1523300141 2014-06-23  402101.0         2       0.75         1020      1350   \n",
+       "291310100  2015-01-16  400000.0         3       2.50         1600      2388   \n",
+       "1523300157 2014-10-15  325000.0         2       0.75         1020      1076   \n",
+       "\n",
+       "            floors waterfront  view  condition          grade  sqft_above  \\\n",
+       "id                                                                          \n",
+       "7129300520     1.0         NO  NONE    Average      7 Average        1180   \n",
+       "6414100192     2.0         NO  NONE    Average      7 Average        2170   \n",
+       "5631500400     1.0         NO  NONE    Average  6 Low Average         770   \n",
+       "2487200875     1.0         NO  NONE  Very Good      7 Average        1050   \n",
+       "1954400510     1.0         NO  NONE    Average         8 Good        1680   \n",
+       "...            ...        ...   ...        ...            ...         ...   \n",
+       "263000018      3.0         NO  NONE    Average         8 Good        1530   \n",
+       "6600060120     2.0         NO  NONE    Average         8 Good        2310   \n",
+       "1523300141     2.0         NO  NONE    Average      7 Average        1020   \n",
+       "291310100      2.0         NO  NONE    Average         8 Good        1600   \n",
+       "1523300157     2.0         NO  NONE    Average      7 Average        1020   \n",
+       "\n",
+       "           sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
+       "id                                                                            \n",
+       "7129300520           0.0      1955           0.0    98178  47.5112 -122.257   \n",
+       "6414100192         400.0      1951        1991.0    98125  47.7210 -122.319   \n",
+       "5631500400           0.0      1933           0.0    98028  47.7379 -122.233   \n",
+       "2487200875         910.0      1965           0.0    98136  47.5208 -122.393   \n",
+       "1954400510           0.0      1987           0.0    98074  47.6168 -122.045   \n",
+       "...                  ...       ...           ...      ...      ...      ...   \n",
+       "263000018            0.0      2009           0.0    98103  47.6993 -122.346   \n",
+       "6600060120           0.0      2014           0.0    98146  47.5107 -122.362   \n",
+       "1523300141           0.0      2009           0.0    98144  47.5944 -122.299   \n",
+       "291310100            0.0      2004           0.0    98027  47.5345 -122.069   \n",
+       "1523300157           0.0      2008           0.0    98144  47.5941 -122.299   \n",
+       "\n",
+       "            sqft_living15  sqft_lot15  \n",
+       "id                                     \n",
+       "7129300520           1340        5650  \n",
+       "6414100192           1690        7639  \n",
+       "5631500400           2720        8062  \n",
+       "2487200875           1360        5000  \n",
+       "1954400510           1800        7503  \n",
+       "...                   ...         ...  \n",
+       "263000018            1530        1509  \n",
+       "6600060120           1830        7200  \n",
+       "1523300141           1020        2007  \n",
+       "291310100            1410        1287  \n",
+       "1523300157           1020        1357  \n",
+       "\n",
+       "[21597 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#  Converting date to datetime format \n",
+    "df['date'] = pd.to_datetime(df['date'])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>2014-10-13</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>2015-02-25</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>2015-02-18</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263000018</th>\n",
+       "      <td>2014-05-21</td>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6600060120</th>\n",
+       "      <td>2015-02-23</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300141</th>\n",
+       "      <td>2014-06-23</td>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291310100</th>\n",
+       "      <td>2015-01-16</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300157</th>\n",
+       "      <td>2014-10-15</td>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+       "id                                                                            \n",
+       "7129300520 2014-10-13  221900.0         3       1.00         1180      5650   \n",
+       "6414100192 2014-12-09  538000.0         3       2.25         2570      7242   \n",
+       "5631500400 2015-02-25  180000.0         2       1.00          770     10000   \n",
+       "2487200875 2014-12-09  604000.0         4       3.00         1960      5000   \n",
+       "1954400510 2015-02-18  510000.0         3       2.00         1680      8080   \n",
+       "...               ...       ...       ...        ...          ...       ...   \n",
+       "263000018  2014-05-21  360000.0         3       2.50         1530      1131   \n",
+       "6600060120 2015-02-23  400000.0         4       2.50         2310      5813   \n",
+       "1523300141 2014-06-23  402101.0         2       0.75         1020      1350   \n",
+       "291310100  2015-01-16  400000.0         3       2.50         1600      2388   \n",
+       "1523300157 2014-10-15  325000.0         2       0.75         1020      1076   \n",
+       "\n",
+       "            floors waterfront  view  condition          grade  sqft_above  \\\n",
+       "id                                                                          \n",
+       "7129300520     1.0         NO  NONE    Average      7 Average        1180   \n",
+       "6414100192     2.0         NO  NONE    Average      7 Average        2170   \n",
+       "5631500400     1.0         NO  NONE    Average  6 Low Average         770   \n",
+       "2487200875     1.0         NO  NONE  Very Good      7 Average        1050   \n",
+       "1954400510     1.0         NO  NONE    Average         8 Good        1680   \n",
+       "...            ...        ...   ...        ...            ...         ...   \n",
+       "263000018      3.0         NO  NONE    Average         8 Good        1530   \n",
+       "6600060120     2.0         NO  NONE    Average         8 Good        2310   \n",
+       "1523300141     2.0         NO  NONE    Average      7 Average        1020   \n",
+       "291310100      2.0         NO  NONE    Average         8 Good        1600   \n",
+       "1523300157     2.0         NO  NONE    Average      7 Average        1020   \n",
+       "\n",
+       "           sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
+       "id                                                                            \n",
+       "7129300520           0.0      1955           0.0    98178  47.5112 -122.257   \n",
+       "6414100192         400.0      1951           1.0    98125  47.7210 -122.319   \n",
+       "5631500400           0.0      1933           0.0    98028  47.7379 -122.233   \n",
+       "2487200875         910.0      1965           0.0    98136  47.5208 -122.393   \n",
+       "1954400510           0.0      1987           0.0    98074  47.6168 -122.045   \n",
+       "...                  ...       ...           ...      ...      ...      ...   \n",
+       "263000018            0.0      2009           0.0    98103  47.6993 -122.346   \n",
+       "6600060120           0.0      2014           0.0    98146  47.5107 -122.362   \n",
+       "1523300141           0.0      2009           0.0    98144  47.5944 -122.299   \n",
+       "291310100            0.0      2004           0.0    98027  47.5345 -122.069   \n",
+       "1523300157           0.0      2008           0.0    98144  47.5941 -122.299   \n",
+       "\n",
+       "            sqft_living15  sqft_lot15  \n",
+       "id                                     \n",
+       "7129300520           1340        5650  \n",
+       "6414100192           1690        7639  \n",
+       "5631500400           2720        8062  \n",
+       "2487200875           1360        5000  \n",
+       "1954400510           1800        7503  \n",
+       "...                   ...         ...  \n",
+       "263000018            1530        1509  \n",
+       "6600060120           1830        7200  \n",
+       "1523300141           1020        2007  \n",
+       "291310100            1410        1287  \n",
+       "1523300157           1020        1357  \n",
+       "\n",
+       "[21597 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert any houses that have been renovated to '1' to indicate true and any houses that have not been renovated to '0' to indicate false.\n",
+    "df['yr_renovated'] = df['yr_renovated'].apply(lambda x: 1 if x > 0 else x)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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  datetime64[ns]\n",
+      " 1   price          21597 non-null  float64       \n",
+      " 2   bedrooms       21597 non-null  int64         \n",
+      " 3   bathrooms      21597 non-null  float64       \n",
+      " 4   sqft_living    21597 non-null  int64         \n",
+      " 5   sqft_lot       21597 non-null  int64         \n",
+      " 6   floors         21597 non-null  float64       \n",
+      " 7   waterfront     21597 non-null  object        \n",
+      " 8   view           21597 non-null  object        \n",
+      " 9   condition      21597 non-null  object        \n",
+      " 10  grade          21597 non-null  object        \n",
+      " 11  sqft_above     21597 non-null  int64         \n",
+      " 12  sqft_basement  21597 non-null  object        \n",
+      " 13  yr_built       21597 non-null  int64         \n",
+      " 14  yr_renovated   21597 non-null  float64       \n",
+      " 15  zipcode        21597 non-null  int64         \n",
+      " 16  lat            21597 non-null  float64       \n",
+      " 17  long           21597 non-null  float64       \n",
+      " 18  sqft_living15  21597 non-null  int64         \n",
+      " 19  sqft_lot15     21597 non-null  int64         \n",
+      "dtypes: datetime64[ns](1), float64(6), int64(8), object(5)\n",
+      "memory usage: 4.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['0.0', '400.0', '910.0', '1530.0', '?', '730.0', '1700.0', '300.0',\n",
+       "       '970.0', '760.0', '720.0', '700.0', '820.0', '780.0', '790.0',\n",
+       "       '330.0', '1620.0', '360.0', '588.0', '1510.0', '410.0', '990.0',\n",
+       "       '600.0', '560.0', '550.0', '1000.0', '1600.0', '500.0', '1040.0',\n",
+       "       '880.0', '1010.0', '240.0', '265.0', '290.0', '800.0', '540.0',\n",
+       "       '710.0', '840.0', '380.0', '770.0', '480.0', '570.0', '1490.0',\n",
+       "       '620.0', '1250.0', '1270.0', '120.0', '650.0', '180.0', '1130.0',\n",
+       "       '450.0', '1640.0', '1460.0', '1020.0', '1030.0', '750.0', '640.0',\n",
+       "       '1070.0', '490.0', '1310.0', '630.0', '2000.0', '390.0', '430.0',\n",
+       "       '850.0', '210.0', '1430.0', '1950.0', '440.0', '220.0', '1160.0',\n",
+       "       '860.0', '580.0', '2060.0', '1820.0', '1180.0', '200.0', '1150.0',\n",
+       "       '1200.0', '680.0', '530.0', '1450.0', '1170.0', '1080.0', '960.0',\n",
+       "       '280.0', '870.0', '1100.0', '460.0', '1400.0', '660.0', '1220.0',\n",
+       "       '900.0', '420.0', '1580.0', '1380.0', '475.0', '690.0', '270.0',\n",
+       "       '350.0', '935.0', '1370.0', '980.0', '1470.0', '160.0', '950.0',\n",
+       "       '50.0', '740.0', '1780.0', '1900.0', '340.0', '470.0', '370.0',\n",
+       "       '140.0', '1760.0', '130.0', '520.0', '890.0', '1110.0', '150.0',\n",
+       "       '1720.0', '810.0', '190.0', '1290.0', '670.0', '1800.0', '1120.0',\n",
+       "       '1810.0', '60.0', '1050.0', '940.0', '310.0', '930.0', '1390.0',\n",
+       "       '610.0', '1830.0', '1300.0', '510.0', '1330.0', '1590.0', '920.0',\n",
+       "       '1320.0', '1420.0', '1240.0', '1960.0', '1560.0', '2020.0',\n",
+       "       '1190.0', '2110.0', '1280.0', '250.0', '2390.0', '1230.0', '170.0',\n",
+       "       '830.0', '1260.0', '1410.0', '1340.0', '590.0', '1500.0', '1140.0',\n",
+       "       '260.0', '100.0', '320.0', '1480.0', '1060.0', '1284.0', '1670.0',\n",
+       "       '1350.0', '2570.0', '1090.0', '110.0', '2500.0', '90.0', '1940.0',\n",
+       "       '1550.0', '2350.0', '2490.0', '1481.0', '1360.0', '1135.0',\n",
+       "       '1520.0', '1850.0', '1660.0', '2130.0', '2600.0', '1690.0',\n",
+       "       '243.0', '1210.0', '1024.0', '1798.0', '1610.0', '1440.0',\n",
+       "       '1570.0', '1650.0', '704.0', '1910.0', '1630.0', '2360.0',\n",
+       "       '1852.0', '2090.0', '2400.0', '1790.0', '2150.0', '230.0', '70.0',\n",
+       "       '1680.0', '2100.0', '3000.0', '1870.0', '1710.0', '2030.0',\n",
+       "       '875.0', '1540.0', '2850.0', '2170.0', '506.0', '906.0', '145.0',\n",
+       "       '2040.0', '784.0', '1750.0', '374.0', '518.0', '2720.0', '2730.0',\n",
+       "       '1840.0', '3480.0', '2160.0', '1920.0', '2330.0', '1860.0',\n",
+       "       '2050.0', '4820.0', '1913.0', '80.0', '2010.0', '3260.0', '2200.0',\n",
+       "       '415.0', '1730.0', '652.0', '2196.0', '1930.0', '515.0', '40.0',\n",
+       "       '2080.0', '2580.0', '1548.0', '1740.0', '235.0', '861.0', '1890.0',\n",
+       "       '2220.0', '792.0', '2070.0', '4130.0', '2250.0', '2240.0',\n",
+       "       '1990.0', '768.0', '2550.0', '435.0', '1008.0', '2300.0', '2610.0',\n",
+       "       '666.0', '3500.0', '172.0', '1816.0', '2190.0', '1245.0', '1525.0',\n",
+       "       '1880.0', '862.0', '946.0', '1281.0', '414.0', '2180.0', '276.0',\n",
+       "       '1248.0', '602.0', '516.0', '176.0', '225.0', '1275.0', '266.0',\n",
+       "       '283.0', '65.0', '2310.0', '10.0', '1770.0', '2120.0', '295.0',\n",
+       "       '207.0', '915.0', '556.0', '417.0', '143.0', '508.0', '2810.0',\n",
+       "       '20.0', '274.0', '248.0'], dtype=object)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['sqft_basement'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "sqft_basement has a '?' value, let's replace it with a 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>2014-10-13</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>2015-02-25</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>2015-02-18</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263000018</th>\n",
+       "      <td>2014-05-21</td>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6600060120</th>\n",
+       "      <td>2015-02-23</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300141</th>\n",
+       "      <td>2014-06-23</td>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291310100</th>\n",
+       "      <td>2015-01-16</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300157</th>\n",
+       "      <td>2014-10-15</td>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+       "id                                                                            \n",
+       "7129300520 2014-10-13  221900.0         3       1.00         1180      5650   \n",
+       "6414100192 2014-12-09  538000.0         3       2.25         2570      7242   \n",
+       "5631500400 2015-02-25  180000.0         2       1.00          770     10000   \n",
+       "2487200875 2014-12-09  604000.0         4       3.00         1960      5000   \n",
+       "1954400510 2015-02-18  510000.0         3       2.00         1680      8080   \n",
+       "...               ...       ...       ...        ...          ...       ...   \n",
+       "263000018  2014-05-21  360000.0         3       2.50         1530      1131   \n",
+       "6600060120 2015-02-23  400000.0         4       2.50         2310      5813   \n",
+       "1523300141 2014-06-23  402101.0         2       0.75         1020      1350   \n",
+       "291310100  2015-01-16  400000.0         3       2.50         1600      2388   \n",
+       "1523300157 2014-10-15  325000.0         2       0.75         1020      1076   \n",
+       "\n",
+       "            floors waterfront  view  condition          grade  sqft_above  \\\n",
+       "id                                                                          \n",
+       "7129300520     1.0         NO  NONE    Average      7 Average        1180   \n",
+       "6414100192     2.0         NO  NONE    Average      7 Average        2170   \n",
+       "5631500400     1.0         NO  NONE    Average  6 Low Average         770   \n",
+       "2487200875     1.0         NO  NONE  Very Good      7 Average        1050   \n",
+       "1954400510     1.0         NO  NONE    Average         8 Good        1680   \n",
+       "...            ...        ...   ...        ...            ...         ...   \n",
+       "263000018      3.0         NO  NONE    Average         8 Good        1530   \n",
+       "6600060120     2.0         NO  NONE    Average         8 Good        2310   \n",
+       "1523300141     2.0         NO  NONE    Average      7 Average        1020   \n",
+       "291310100      2.0         NO  NONE    Average         8 Good        1600   \n",
+       "1523300157     2.0         NO  NONE    Average      7 Average        1020   \n",
+       "\n",
+       "            sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
+       "id                                                                             \n",
+       "7129300520            0.0      1955           0.0    98178  47.5112 -122.257   \n",
+       "6414100192          400.0      1951           1.0    98125  47.7210 -122.319   \n",
+       "5631500400            0.0      1933           0.0    98028  47.7379 -122.233   \n",
+       "2487200875          910.0      1965           0.0    98136  47.5208 -122.393   \n",
+       "1954400510            0.0      1987           0.0    98074  47.6168 -122.045   \n",
+       "...                   ...       ...           ...      ...      ...      ...   \n",
+       "263000018             0.0      2009           0.0    98103  47.6993 -122.346   \n",
+       "6600060120            0.0      2014           0.0    98146  47.5107 -122.362   \n",
+       "1523300141            0.0      2009           0.0    98144  47.5944 -122.299   \n",
+       "291310100             0.0      2004           0.0    98027  47.5345 -122.069   \n",
+       "1523300157            0.0      2008           0.0    98144  47.5941 -122.299   \n",
+       "\n",
+       "            sqft_living15  sqft_lot15  \n",
+       "id                                     \n",
+       "7129300520           1340        5650  \n",
+       "6414100192           1690        7639  \n",
+       "5631500400           2720        8062  \n",
+       "2487200875           1360        5000  \n",
+       "1954400510           1800        7503  \n",
+       "...                   ...         ...  \n",
+       "263000018            1530        1509  \n",
+       "6600060120           1830        7200  \n",
+       "1523300141           1020        2007  \n",
+       "291310100            1410        1287  \n",
+       "1523300157           1020        1357  \n",
+       "\n",
+       "[21597 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert sqft_basement to int and replace ? with 0\n",
+    "df['sqft_basement'] = df['sqft_basement'].replace({'?':np.nan}).astype(float)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>2014-10-13</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>2015-02-25</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>2015-02-18</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263000018</th>\n",
+       "      <td>2014-05-21</td>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6600060120</th>\n",
+       "      <td>2015-02-23</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300141</th>\n",
+       "      <td>2014-06-23</td>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291310100</th>\n",
+       "      <td>2015-01-16</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300157</th>\n",
+       "      <td>2014-10-15</td>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+       "id                                                                            \n",
+       "7129300520 2014-10-13  221900.0         3       1.00         1180      5650   \n",
+       "6414100192 2014-12-09  538000.0         3       2.25         2570      7242   \n",
+       "5631500400 2015-02-25  180000.0         2       1.00          770     10000   \n",
+       "2487200875 2014-12-09  604000.0         4       3.00         1960      5000   \n",
+       "1954400510 2015-02-18  510000.0         3       2.00         1680      8080   \n",
+       "...               ...       ...       ...        ...          ...       ...   \n",
+       "263000018  2014-05-21  360000.0         3       2.50         1530      1131   \n",
+       "6600060120 2015-02-23  400000.0         4       2.50         2310      5813   \n",
+       "1523300141 2014-06-23  402101.0         2       0.75         1020      1350   \n",
+       "291310100  2015-01-16  400000.0         3       2.50         1600      2388   \n",
+       "1523300157 2014-10-15  325000.0         2       0.75         1020      1076   \n",
+       "\n",
+       "            floors waterfront  view  condition  ... sqft_basement  yr_built  \\\n",
+       "id                                              ...                           \n",
+       "7129300520     1.0         NO  NONE    Average  ...           0.0      1955   \n",
+       "6414100192     2.0         NO  NONE    Average  ...         400.0      1951   \n",
+       "5631500400     1.0         NO  NONE    Average  ...           0.0      1933   \n",
+       "2487200875     1.0         NO  NONE  Very Good  ...         910.0      1965   \n",
+       "1954400510     1.0         NO  NONE    Average  ...           0.0      1987   \n",
+       "...            ...        ...   ...        ...  ...           ...       ...   \n",
+       "263000018      3.0         NO  NONE    Average  ...           0.0      2009   \n",
+       "6600060120     2.0         NO  NONE    Average  ...           0.0      2014   \n",
+       "1523300141     2.0         NO  NONE    Average  ...           0.0      2009   \n",
+       "291310100      2.0         NO  NONE    Average  ...           0.0      2004   \n",
+       "1523300157     2.0         NO  NONE    Average  ...           0.0      2008   \n",
+       "\n",
+       "            yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "id                                                                   \n",
+       "7129300520           0.0    98178  47.5112 -122.257           1340   \n",
+       "6414100192           1.0    98125  47.7210 -122.319           1690   \n",
+       "5631500400           0.0    98028  47.7379 -122.233           2720   \n",
+       "2487200875           0.0    98136  47.5208 -122.393           1360   \n",
+       "1954400510           0.0    98074  47.6168 -122.045           1800   \n",
+       "...                  ...      ...      ...      ...            ...   \n",
+       "263000018            0.0    98103  47.6993 -122.346           1530   \n",
+       "6600060120           0.0    98146  47.5107 -122.362           1830   \n",
+       "1523300141           0.0    98144  47.5944 -122.299           1020   \n",
+       "291310100            0.0    98027  47.5345 -122.069           1410   \n",
+       "1523300157           0.0    98144  47.5941 -122.299           1020   \n",
+       "\n",
+       "            sqft_lot15  month_of_date  year_of_date  \n",
+       "id                                                   \n",
+       "7129300520        5650             10          2014  \n",
+       "6414100192        7639             12          2014  \n",
+       "5631500400        8062              2          2015  \n",
+       "2487200875        5000             12          2014  \n",
+       "1954400510        7503              2          2015  \n",
+       "...                ...            ...           ...  \n",
+       "263000018         1509              5          2014  \n",
+       "6600060120        7200              2          2015  \n",
+       "1523300141        2007              6          2014  \n",
+       "291310100         1287              1          2015  \n",
+       "1523300157        1357             10          2014  \n",
+       "\n",
+       "[21597 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Retrieve the month and year from the date column.\n",
+    "df['month_of_date'] = pd.DatetimeIndex(df['date']).month\n",
+    "df['year_of_date'] = pd.DatetimeIndex(df['date']).year\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263000018</th>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6600060120</th>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300141</th>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291310100</th>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300157</th>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
+       "id                                                                         \n",
+       "7129300520  221900.0         3       1.00         1180      5650     1.0   \n",
+       "6414100192  538000.0         3       2.25         2570      7242     2.0   \n",
+       "5631500400  180000.0         2       1.00          770     10000     1.0   \n",
+       "2487200875  604000.0         4       3.00         1960      5000     1.0   \n",
+       "1954400510  510000.0         3       2.00         1680      8080     1.0   \n",
+       "...              ...       ...        ...          ...       ...     ...   \n",
+       "263000018   360000.0         3       2.50         1530      1131     3.0   \n",
+       "6600060120  400000.0         4       2.50         2310      5813     2.0   \n",
+       "1523300141  402101.0         2       0.75         1020      1350     2.0   \n",
+       "291310100   400000.0         3       2.50         1600      2388     2.0   \n",
+       "1523300157  325000.0         2       0.75         1020      1076     2.0   \n",
+       "\n",
+       "           waterfront  view  condition          grade  ...  sqft_basement  \\\n",
+       "id                                                     ...                  \n",
+       "7129300520         NO  NONE    Average      7 Average  ...            0.0   \n",
+       "6414100192         NO  NONE    Average      7 Average  ...          400.0   \n",
+       "5631500400         NO  NONE    Average  6 Low Average  ...            0.0   \n",
+       "2487200875         NO  NONE  Very Good      7 Average  ...          910.0   \n",
+       "1954400510         NO  NONE    Average         8 Good  ...            0.0   \n",
+       "...               ...   ...        ...            ...  ...            ...   \n",
+       "263000018          NO  NONE    Average         8 Good  ...            0.0   \n",
+       "6600060120         NO  NONE    Average         8 Good  ...            0.0   \n",
+       "1523300141         NO  NONE    Average      7 Average  ...            0.0   \n",
+       "291310100          NO  NONE    Average         8 Good  ...            0.0   \n",
+       "1523300157         NO  NONE    Average      7 Average  ...            0.0   \n",
+       "\n",
+       "            yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "id                                                                             \n",
+       "7129300520      1955           0.0    98178  47.5112 -122.257           1340   \n",
+       "6414100192      1951           1.0    98125  47.7210 -122.319           1690   \n",
+       "5631500400      1933           0.0    98028  47.7379 -122.233           2720   \n",
+       "2487200875      1965           0.0    98136  47.5208 -122.393           1360   \n",
+       "1954400510      1987           0.0    98074  47.6168 -122.045           1800   \n",
+       "...              ...           ...      ...      ...      ...            ...   \n",
+       "263000018       2009           0.0    98103  47.6993 -122.346           1530   \n",
+       "6600060120      2014           0.0    98146  47.5107 -122.362           1830   \n",
+       "1523300141      2009           0.0    98144  47.5944 -122.299           1020   \n",
+       "291310100       2004           0.0    98027  47.5345 -122.069           1410   \n",
+       "1523300157      2008           0.0    98144  47.5941 -122.299           1020   \n",
+       "\n",
+       "            sqft_lot15  month_of_date  year_of_date  \n",
+       "id                                                   \n",
+       "7129300520        5650             10          2014  \n",
+       "6414100192        7639             12          2014  \n",
+       "5631500400        8062              2          2015  \n",
+       "2487200875        5000             12          2014  \n",
+       "1954400510        7503              2          2015  \n",
+       "...                ...            ...           ...  \n",
+       "263000018         1509              5          2014  \n",
+       "6600060120        7200              2          2015  \n",
+       "1523300141        2007              6          2014  \n",
+       "291310100         1287              1          2015  \n",
+       "1523300157        1357             10          2014  \n",
+       "\n",
+       "[21597 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Drop the date column.\n",
+    "df.drop(columns=['date'], inplace=True)\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Average', 'Very Good', 'Good', 'Poor', 'Fair'], dtype=object)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Inspecting the condition column\n",
+    "df['condition'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
+       "id                                                                         \n",
+       "7129300520  221900.0         3       1.00         1180      5650     1.0   \n",
+       "6414100192  538000.0         3       2.25         2570      7242     2.0   \n",
+       "5631500400  180000.0         2       1.00          770     10000     1.0   \n",
+       "2487200875  604000.0         4       3.00         1960      5000     1.0   \n",
+       "1954400510  510000.0         3       2.00         1680      8080     1.0   \n",
+       "\n",
+       "           waterfront  view  condition          grade  ...  sqft_basement  \\\n",
+       "id                                                     ...                  \n",
+       "7129300520         NO  NONE          3      7 Average  ...            0.0   \n",
+       "6414100192         NO  NONE          3      7 Average  ...          400.0   \n",
+       "5631500400         NO  NONE          3  6 Low Average  ...            0.0   \n",
+       "2487200875         NO  NONE          1      7 Average  ...          910.0   \n",
+       "1954400510         NO  NONE          3         8 Good  ...            0.0   \n",
+       "\n",
+       "            yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "id                                                                             \n",
+       "7129300520      1955           0.0    98178  47.5112 -122.257           1340   \n",
+       "6414100192      1951           1.0    98125  47.7210 -122.319           1690   \n",
+       "5631500400      1933           0.0    98028  47.7379 -122.233           2720   \n",
+       "2487200875      1965           0.0    98136  47.5208 -122.393           1360   \n",
+       "1954400510      1987           0.0    98074  47.6168 -122.045           1800   \n",
+       "\n",
+       "            sqft_lot15  month_of_date  year_of_date  \n",
+       "id                                                   \n",
+       "7129300520        5650             10          2014  \n",
+       "6414100192        7639             12          2014  \n",
+       "5631500400        8062              2          2015  \n",
+       "2487200875        5000             12          2014  \n",
+       "1954400510        7503              2          2015  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Mapping conditions with the respective number\n",
+    "# Ratings mapping\n",
+    "ratings_mapping = {\n",
+    "    'Average': 3,\n",
+    "    'Very Good': 1,\n",
+    "    'Good': 2,\n",
+    "    'Poor': 4,\n",
+    "    'Fair': 5\n",
+    "}\n",
+    "\n",
+    "# Replace categorical values with numerical values\n",
+    "df['condition'] = df['condition'].replace(ratings_mapping)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['NO', 'YES'], dtype=object)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Inspecting the waterfront column\n",
+    "df['waterfront'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
+       "id                                                                         \n",
+       "7129300520  221900.0         3       1.00         1180      5650     1.0   \n",
+       "6414100192  538000.0         3       2.25         2570      7242     2.0   \n",
+       "5631500400  180000.0         2       1.00          770     10000     1.0   \n",
+       "2487200875  604000.0         4       3.00         1960      5000     1.0   \n",
+       "1954400510  510000.0         3       2.00         1680      8080     1.0   \n",
+       "\n",
+       "            waterfront  view  condition          grade  ...  sqft_basement  \\\n",
+       "id                                                      ...                  \n",
+       "7129300520           0  NONE          3      7 Average  ...            0.0   \n",
+       "6414100192           0  NONE          3      7 Average  ...          400.0   \n",
+       "5631500400           0  NONE          3  6 Low Average  ...            0.0   \n",
+       "2487200875           0  NONE          1      7 Average  ...          910.0   \n",
+       "1954400510           0  NONE          3         8 Good  ...            0.0   \n",
+       "\n",
+       "            yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "id                                                                             \n",
+       "7129300520      1955           0.0    98178  47.5112 -122.257           1340   \n",
+       "6414100192      1951           1.0    98125  47.7210 -122.319           1690   \n",
+       "5631500400      1933           0.0    98028  47.7379 -122.233           2720   \n",
+       "2487200875      1965           0.0    98136  47.5208 -122.393           1360   \n",
+       "1954400510      1987           0.0    98074  47.6168 -122.045           1800   \n",
+       "\n",
+       "            sqft_lot15  month_of_date  year_of_date  \n",
+       "id                                                   \n",
+       "7129300520        5650             10          2014  \n",
+       "6414100192        7639             12          2014  \n",
+       "5631500400        8062              2          2015  \n",
+       "2487200875        5000             12          2014  \n",
+       "1954400510        7503              2          2015  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Mapping waterfront with the respective number \n",
+    "# Replacing YES with 1 and NO with 0\n",
+    "df['waterfront'] = df['waterfront'].astype(str).replace({'YES': 1, 'NO': 0})\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['NONE', 'GOOD', 'EXCELLENT', 'AVERAGE', 'FAIR'], dtype=object)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Inspecting the view column\n",
+    "df['view'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
+       "id                                                                         \n",
+       "7129300520  221900.0         3       1.00         1180      5650     1.0   \n",
+       "6414100192  538000.0         3       2.25         2570      7242     2.0   \n",
+       "5631500400  180000.0         2       1.00          770     10000     1.0   \n",
+       "2487200875  604000.0         4       3.00         1960      5000     1.0   \n",
+       "1954400510  510000.0         3       2.00         1680      8080     1.0   \n",
+       "\n",
+       "            waterfront  view  condition          grade  ...  sqft_basement  \\\n",
+       "id                                                      ...                  \n",
+       "7129300520           0     0          3      7 Average  ...            0.0   \n",
+       "6414100192           0     0          3      7 Average  ...          400.0   \n",
+       "5631500400           0     0          3  6 Low Average  ...            0.0   \n",
+       "2487200875           0     0          1      7 Average  ...          910.0   \n",
+       "1954400510           0     0          3         8 Good  ...            0.0   \n",
+       "\n",
+       "            yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "id                                                                             \n",
+       "7129300520      1955           0.0    98178  47.5112 -122.257           1340   \n",
+       "6414100192      1951           1.0    98125  47.7210 -122.319           1690   \n",
+       "5631500400      1933           0.0    98028  47.7379 -122.233           2720   \n",
+       "2487200875      1965           0.0    98136  47.5208 -122.393           1360   \n",
+       "1954400510      1987           0.0    98074  47.6168 -122.045           1800   \n",
+       "\n",
+       "            sqft_lot15  month_of_date  year_of_date  \n",
+       "id                                                   \n",
+       "7129300520        5650             10          2014  \n",
+       "6414100192        7639             12          2014  \n",
+       "5631500400        8062              2          2015  \n",
+       "2487200875        5000             12          2014  \n",
+       "1954400510        7503              2          2015  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define the mappings\n",
+    "quality_mapping = {\n",
+    "    'NONE': 0,\n",
+    "    'GOOD': 1,\n",
+    "    'EXCELLENT': 2,\n",
+    "    'AVERAGE': 3,\n",
+    "    'FAIR': 4\n",
+    "}\n",
+    "\n",
+    "# Replace the values using the mapping\n",
+    "df['view'] = df['view'].replace(quality_mapping)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['7 Average', '6 Low Average', '8 Good', '11 Excellent', '9 Better',\n",
+       "       '5 Fair', '10 Very Good', '12 Luxury', '4 Low', '3 Poor',\n",
+       "       '13 Mansion'], dtype=object)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Inspecting the grade column\n",
+    "df['grade'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263000018</th>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6600060120</th>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300141</th>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291310100</th>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1523300157</th>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
+       "id                                                                         \n",
+       "7129300520  221900.0         3       1.00         1180      5650     1.0   \n",
+       "6414100192  538000.0         3       2.25         2570      7242     2.0   \n",
+       "5631500400  180000.0         2       1.00          770     10000     1.0   \n",
+       "2487200875  604000.0         4       3.00         1960      5000     1.0   \n",
+       "1954400510  510000.0         3       2.00         1680      8080     1.0   \n",
+       "...              ...       ...        ...          ...       ...     ...   \n",
+       "263000018   360000.0         3       2.50         1530      1131     3.0   \n",
+       "6600060120  400000.0         4       2.50         2310      5813     2.0   \n",
+       "1523300141  402101.0         2       0.75         1020      1350     2.0   \n",
+       "291310100   400000.0         3       2.50         1600      2388     2.0   \n",
+       "1523300157  325000.0         2       0.75         1020      1076     2.0   \n",
+       "\n",
+       "            waterfront  view  condition  grade  ...  sqft_basement  yr_built  \\\n",
+       "id                                              ...                            \n",
+       "7129300520           0     0          3      7  ...            0.0      1955   \n",
+       "6414100192           0     0          3      7  ...          400.0      1951   \n",
+       "5631500400           0     0          3      6  ...            0.0      1933   \n",
+       "2487200875           0     0          1      7  ...          910.0      1965   \n",
+       "1954400510           0     0          3      8  ...            0.0      1987   \n",
+       "...                ...   ...        ...    ...  ...            ...       ...   \n",
+       "263000018            0     0          3      8  ...            0.0      2009   \n",
+       "6600060120           0     0          3      8  ...            0.0      2014   \n",
+       "1523300141           0     0          3      7  ...            0.0      2009   \n",
+       "291310100            0     0          3      8  ...            0.0      2004   \n",
+       "1523300157           0     0          3      7  ...            0.0      2008   \n",
+       "\n",
+       "            yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+       "id                                                                   \n",
+       "7129300520           0.0    98178  47.5112 -122.257           1340   \n",
+       "6414100192           1.0    98125  47.7210 -122.319           1690   \n",
+       "5631500400           0.0    98028  47.7379 -122.233           2720   \n",
+       "2487200875           0.0    98136  47.5208 -122.393           1360   \n",
+       "1954400510           0.0    98074  47.6168 -122.045           1800   \n",
+       "...                  ...      ...      ...      ...            ...   \n",
+       "263000018            0.0    98103  47.6993 -122.346           1530   \n",
+       "6600060120           0.0    98146  47.5107 -122.362           1830   \n",
+       "1523300141           0.0    98144  47.5944 -122.299           1020   \n",
+       "291310100            0.0    98027  47.5345 -122.069           1410   \n",
+       "1523300157           0.0    98144  47.5941 -122.299           1020   \n",
+       "\n",
+       "            sqft_lot15  month_of_date  year_of_date  \n",
+       "id                                                   \n",
+       "7129300520        5650             10          2014  \n",
+       "6414100192        7639             12          2014  \n",
+       "5631500400        8062              2          2015  \n",
+       "2487200875        5000             12          2014  \n",
+       "1954400510        7503              2          2015  \n",
+       "...                ...            ...           ...  \n",
+       "263000018         1509              5          2014  \n",
+       "6600060120        7200              2          2015  \n",
+       "1523300141        2007              6          2014  \n",
+       "291310100         1287              1          2015  \n",
+       "1523300157        1357             10          2014  \n",
+       "\n",
+       "[21597 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define the mappings\n",
+    "rating_mapping = {\n",
+    "    'Average': 7,\n",
+    "    'Low Average': 6,\n",
+    "    'Good': 8,\n",
+    "    'Excellent': 11,\n",
+    "    'Better': 9,\n",
+    "    'Fair': 5,\n",
+    "    'Very Good': 10,\n",
+    "    'Luxury': 12,\n",
+    "    'Low': 4,\n",
+    "    'Poor': 3,\n",
+    "    'Mansion': 13\n",
+    "}\n",
+    "\n",
+    "# Extract the rating string and replace with the corresponding numerical value\n",
+    "df['grade'] = df['grade'].str.extract('(\\d+)').astype(int)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 21597 entries, 7129300520 to 1523300157\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   price          21597 non-null  float64\n",
+      " 1   bedrooms       21597 non-null  int64  \n",
+      " 2   bathrooms      21597 non-null  float64\n",
+      " 3   sqft_living    21597 non-null  int64  \n",
+      " 4   sqft_lot       21597 non-null  int64  \n",
+      " 5   floors         21597 non-null  float64\n",
+      " 6   waterfront     21597 non-null  int64  \n",
+      " 7   view           21597 non-null  int64  \n",
+      " 8   condition      21597 non-null  int64  \n",
+      " 9   grade          21597 non-null  int64  \n",
+      " 10  sqft_above     21597 non-null  int64  \n",
+      " 11  sqft_basement  21143 non-null  float64\n",
+      " 12  yr_built       21597 non-null  int64  \n",
+      " 13  yr_renovated   21597 non-null  float64\n",
+      " 14  zipcode        21597 non-null  int64  \n",
+      " 15  lat            21597 non-null  float64\n",
+      " 16  long           21597 non-null  float64\n",
+      " 17  sqft_living15  21597 non-null  int64  \n",
+      " 18  sqft_lot15     21597 non-null  int64  \n",
+      " 19  month_of_date  21597 non-null  int64  \n",
+      " 20  year_of_date   21597 non-null  int64  \n",
+      "dtypes: float64(7), int64(14)\n",
+      "memory usage: 4.2 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sqft_living      0.701917\n",
+       "grade            0.667951\n",
+       "sqft_above       0.605368\n",
+       "sqft_living15    0.585241\n",
+       "bathrooms        0.525906\n",
+       "sqft_basement    0.325008\n",
+       "bedrooms         0.308787\n",
+       "lat              0.306692\n",
+       "view             0.290620\n",
+       "waterfront       0.264306\n",
+       "floors           0.256804\n",
+       "yr_renovated     0.117543\n",
+       "sqft_lot         0.089876\n",
+       "sqft_lot15       0.082845\n",
+       "yr_built         0.053953\n",
+       "zipcode          0.053402\n",
+       "condition        0.040742\n",
+       "long             0.022036\n",
+       "month_of_date    0.009928\n",
+       "year_of_date     0.003727\n",
+       "Name: price, dtype: float64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Checking the correlation between price (target) and predictors\n",
+    "df.corr()['price'].drop(['price']).map(abs).sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "sqft_living, grade, sqft_above have the highest correlation with the target while year_of_date, month_of_date and long have the lowest correlation with the target."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here - remember to use markdown cells for comments as well!"
+    "# Identfying Numeric and categorical columns\n",
+    "numeric = ['bedrooms', \n",
+    "           'bathrooms', \n",
+    "           'sqft_living', \n",
+    "           'sqft_lot', \n",
+    "           'sqft_above', \n",
+    "           'sqft_basement',\n",
+    "           'lat', \n",
+    "           'long',\n",
+    "           'sqft_living15', \n",
+    "           'sqft_lot15']\n",
+    "\n",
+    "categorical = ['floors',\n",
+    "               'waterfront', \n",
+    "               'view', \n",
+    "               'condition', \n",
+    "               'grade',\n",
+    "               'yr_renovated',\n",
+    "               'zipcode',\n",
+    "               'month_of_date']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>month_of_date</th>\n",
+       "      <th>year_of_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21592</th>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6993</td>\n",
+       "      <td>-122.346</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>1509</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21593</th>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>5813</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5107</td>\n",
+       "      <td>-122.362</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>7200</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21594</th>\n",
+       "      <td>402101.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5944</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21595</th>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>2388</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98027</td>\n",
+       "      <td>47.5345</td>\n",
+       "      <td>-122.069</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21596</th>\n",
+       "      <td>325000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1076</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98144</td>\n",
+       "      <td>47.5941</td>\n",
+       "      <td>-122.299</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>1357</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>21597 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
+       "0      221900.0         3       1.00         1180      5650     1.0   \n",
+       "1      538000.0         3       2.25         2570      7242     2.0   \n",
+       "2      180000.0         2       1.00          770     10000     1.0   \n",
+       "3      604000.0         4       3.00         1960      5000     1.0   \n",
+       "4      510000.0         3       2.00         1680      8080     1.0   \n",
+       "...         ...       ...        ...          ...       ...     ...   \n",
+       "21592  360000.0         3       2.50         1530      1131     3.0   \n",
+       "21593  400000.0         4       2.50         2310      5813     2.0   \n",
+       "21594  402101.0         2       0.75         1020      1350     2.0   \n",
+       "21595  400000.0         3       2.50         1600      2388     2.0   \n",
+       "21596  325000.0         2       0.75         1020      1076     2.0   \n",
+       "\n",
+       "       waterfront  view  condition  grade  ...  sqft_basement  yr_built  \\\n",
+       "0               0     0          3      7  ...            0.0      1955   \n",
+       "1               0     0          3      7  ...          400.0      1951   \n",
+       "2               0     0          3      6  ...            0.0      1933   \n",
+       "3               0     0          1      7  ...          910.0      1965   \n",
+       "4               0     0          3      8  ...            0.0      1987   \n",
+       "...           ...   ...        ...    ...  ...            ...       ...   \n",
+       "21592           0     0          3      8  ...            0.0      2009   \n",
+       "21593           0     0          3      8  ...            0.0      2014   \n",
+       "21594           0     0          3      7  ...            0.0      2009   \n",
+       "21595           0     0          3      8  ...            0.0      2004   \n",
+       "21596           0     0          3      7  ...            0.0      2008   \n",
+       "\n",
+       "       yr_renovated  zipcode      lat     long  sqft_living15  sqft_lot15  \\\n",
+       "0               0.0    98178  47.5112 -122.257           1340        5650   \n",
+       "1               1.0    98125  47.7210 -122.319           1690        7639   \n",
+       "2               0.0    98028  47.7379 -122.233           2720        8062   \n",
+       "3               0.0    98136  47.5208 -122.393           1360        5000   \n",
+       "4               0.0    98074  47.6168 -122.045           1800        7503   \n",
+       "...             ...      ...      ...      ...            ...         ...   \n",
+       "21592           0.0    98103  47.6993 -122.346           1530        1509   \n",
+       "21593           0.0    98146  47.5107 -122.362           1830        7200   \n",
+       "21594           0.0    98144  47.5944 -122.299           1020        2007   \n",
+       "21595           0.0    98027  47.5345 -122.069           1410        1287   \n",
+       "21596           0.0    98144  47.5941 -122.299           1020        1357   \n",
+       "\n",
+       "       month_of_date  year_of_date  \n",
+       "0                 10          2014  \n",
+       "1                 12          2014  \n",
+       "2                  2          2015  \n",
+       "3                 12          2014  \n",
+       "4                  2          2015  \n",
+       "...              ...           ...  \n",
+       "21592              5          2014  \n",
+       "21593              2          2015  \n",
+       "21594              6          2014  \n",
+       "21595              1          2015  \n",
+       "21596             10          2014  \n",
+       "\n",
+       "[21597 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#reset index and dropping the id column\n",
+    "df.reset_index(inplace=True, drop=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 21597 entries, 0 to 21596\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   price          21597 non-null  float64\n",
+      " 1   bedrooms       21597 non-null  int64  \n",
+      " 2   bathrooms      21597 non-null  float64\n",
+      " 3   sqft_living    21597 non-null  int64  \n",
+      " 4   sqft_lot       21597 non-null  int64  \n",
+      " 5   floors         21597 non-null  float64\n",
+      " 6   waterfront     21597 non-null  int64  \n",
+      " 7   view           21597 non-null  int64  \n",
+      " 8   condition      21597 non-null  int64  \n",
+      " 9   grade          21597 non-null  int64  \n",
+      " 10  sqft_above     21597 non-null  int64  \n",
+      " 11  sqft_basement  21143 non-null  float64\n",
+      " 12  yr_built       21597 non-null  int64  \n",
+      " 13  yr_renovated   21597 non-null  float64\n",
+      " 14  zipcode        21597 non-null  int64  \n",
+      " 15  lat            21597 non-null  float64\n",
+      " 16  long           21597 non-null  float64\n",
+      " 17  sqft_living15  21597 non-null  int64  \n",
+      " 18  sqft_lot15     21597 non-null  int64  \n",
+      " 19  month_of_date  21597 non-null  int64  \n",
+      " 20  year_of_date   21597 non-null  int64  \n",
+      "dtypes: float64(7), int64(14)\n",
+      "memory usage: 3.5 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MODELLING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***1. Linear Regression model***\n",
+    "\n",
+    "We will pick **`sqft_living` - Square footage of living space in the home** to be used to create our linear regression model because it has the most correlation with the price and it has the most linear scatter plor hence a good candidate."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "learn-env",
    "language": "python",
    "name": "python3"
   },
@@ -40,7 +5222,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,