diff --git a/HML075-californiahousingproject.ipynb b/HML075-californiahousingproject.ipynb
new file mode 100644
index 0000000..e701b3f
--- /dev/null
+++ b/HML075-californiahousingproject.ipynb
@@ -0,0 +1,1130 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from math import sqrt\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.metrics import r2_score, mean_squared_error\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing=pd.read_csv(\"C:\\\\Users\\\\RUKVITH\\\\Downloads\\\\housing.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20640, 10)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-122.23</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>41</td>\n",
+       "      <td>880</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>322</td>\n",
+       "      <td>126</td>\n",
+       "      <td>8.3252</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "      <td>452600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-122.22</td>\n",
+       "      <td>37.86</td>\n",
+       "      <td>21</td>\n",
+       "      <td>7099</td>\n",
+       "      <td>1106.0</td>\n",
+       "      <td>2401</td>\n",
+       "      <td>1138</td>\n",
+       "      <td>8.3014</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "      <td>358500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-122.24</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>496</td>\n",
+       "      <td>177</td>\n",
+       "      <td>7.2574</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "      <td>352100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52</td>\n",
+       "      <td>1274</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>558</td>\n",
+       "      <td>219</td>\n",
+       "      <td>5.6431</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "      <td>341300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52</td>\n",
+       "      <td>1627</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>565</td>\n",
+       "      <td>259</td>\n",
+       "      <td>3.8462</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "      <td>342200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52</td>\n",
+       "      <td>919</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>413</td>\n",
+       "      <td>193</td>\n",
+       "      <td>4.0368</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "      <td>269700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.84</td>\n",
+       "      <td>52</td>\n",
+       "      <td>2535</td>\n",
+       "      <td>489.0</td>\n",
+       "      <td>1094</td>\n",
+       "      <td>514</td>\n",
+       "      <td>3.6591</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "      <td>299200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.84</td>\n",
+       "      <td>52</td>\n",
+       "      <td>3104</td>\n",
+       "      <td>687.0</td>\n",
+       "      <td>1157</td>\n",
+       "      <td>647</td>\n",
+       "      <td>3.1200</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "      <td>241400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-122.26</td>\n",
+       "      <td>37.84</td>\n",
+       "      <td>42</td>\n",
+       "      <td>2555</td>\n",
+       "      <td>665.0</td>\n",
+       "      <td>1206</td>\n",
+       "      <td>595</td>\n",
+       "      <td>2.0804</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "      <td>226700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.84</td>\n",
+       "      <td>52</td>\n",
+       "      <td>3549</td>\n",
+       "      <td>707.0</td>\n",
+       "      <td>1551</td>\n",
+       "      <td>714</td>\n",
+       "      <td>3.6912</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "      <td>261100</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "0    -122.23     37.88                  41          880           129.0   \n",
+       "1    -122.22     37.86                  21         7099          1106.0   \n",
+       "2    -122.24     37.85                  52         1467           190.0   \n",
+       "3    -122.25     37.85                  52         1274           235.0   \n",
+       "4    -122.25     37.85                  52         1627           280.0   \n",
+       "5    -122.25     37.85                  52          919           213.0   \n",
+       "6    -122.25     37.84                  52         2535           489.0   \n",
+       "7    -122.25     37.84                  52         3104           687.0   \n",
+       "8    -122.26     37.84                  42         2555           665.0   \n",
+       "9    -122.25     37.84                  52         3549           707.0   \n",
+       "\n",
+       "   population  households  median_income ocean_proximity  median_house_value  \n",
+       "0         322         126         8.3252        NEAR BAY              452600  \n",
+       "1        2401        1138         8.3014        NEAR BAY              358500  \n",
+       "2         496         177         7.2574        NEAR BAY              352100  \n",
+       "3         558         219         5.6431        NEAR BAY              341300  \n",
+       "4         565         259         3.8462        NEAR BAY              342200  \n",
+       "5         413         193         4.0368        NEAR BAY              269700  \n",
+       "6        1094         514         3.6591        NEAR BAY              299200  \n",
+       "7        1157         647         3.1200        NEAR BAY              241400  \n",
+       "8        1206         595         2.0804        NEAR BAY              226700  \n",
+       "9        1551         714         3.6912        NEAR BAY              261100  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20433.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-119.569704</td>\n",
+       "      <td>35.631861</td>\n",
+       "      <td>28.639486</td>\n",
+       "      <td>2635.763081</td>\n",
+       "      <td>537.870553</td>\n",
+       "      <td>1425.476744</td>\n",
+       "      <td>499.539680</td>\n",
+       "      <td>3.870671</td>\n",
+       "      <td>206855.816909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.003532</td>\n",
+       "      <td>2.135952</td>\n",
+       "      <td>12.585558</td>\n",
+       "      <td>2181.615252</td>\n",
+       "      <td>421.385070</td>\n",
+       "      <td>1132.462122</td>\n",
+       "      <td>382.329753</td>\n",
+       "      <td>1.899822</td>\n",
+       "      <td>115395.615874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-124.350000</td>\n",
+       "      <td>32.540000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.499900</td>\n",
+       "      <td>14999.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-121.800000</td>\n",
+       "      <td>33.930000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>1447.750000</td>\n",
+       "      <td>296.000000</td>\n",
+       "      <td>787.000000</td>\n",
+       "      <td>280.000000</td>\n",
+       "      <td>2.563400</td>\n",
+       "      <td>119600.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>-118.490000</td>\n",
+       "      <td>34.260000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>2127.000000</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>1166.000000</td>\n",
+       "      <td>409.000000</td>\n",
+       "      <td>3.534800</td>\n",
+       "      <td>179700.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>-118.010000</td>\n",
+       "      <td>37.710000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>3148.000000</td>\n",
+       "      <td>647.000000</td>\n",
+       "      <td>1725.000000</td>\n",
+       "      <td>605.000000</td>\n",
+       "      <td>4.743250</td>\n",
+       "      <td>264725.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>-114.310000</td>\n",
+       "      <td>41.950000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>39320.000000</td>\n",
+       "      <td>6445.000000</td>\n",
+       "      <td>35682.000000</td>\n",
+       "      <td>6082.000000</td>\n",
+       "      <td>15.000100</td>\n",
+       "      <td>500001.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
+       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
+       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
+       "std        2.003532      2.135952           12.585558   2181.615252   \n",
+       "min     -124.350000     32.540000            1.000000      2.000000   \n",
+       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
+       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
+       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
+       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
+       "\n",
+       "       total_bedrooms    population    households  median_income  \\\n",
+       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
+       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
+       "std        421.385070   1132.462122    382.329753       1.899822   \n",
+       "min          1.000000      3.000000      1.000000       0.499900   \n",
+       "25%        296.000000    787.000000    280.000000       2.563400   \n",
+       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
+       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
+       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
+       "\n",
+       "       median_house_value  \n",
+       "count        20640.000000  \n",
+       "mean        206855.816909  \n",
+       "std         115395.615874  \n",
+       "min          14999.000000  \n",
+       "25%         119600.000000  \n",
+       "50%         179700.000000  \n",
+       "75%         264725.000000  \n",
+       "max         500001.000000  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1080 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "housing.hist(figsize=(20,15), color = 'red')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "longitude               0\n",
+       "latitude                0\n",
+       "housing_median_age      0\n",
+       "total_rooms             0\n",
+       "total_bedrooms        207\n",
+       "population              0\n",
+       "households              0\n",
+       "median_income           0\n",
+       "ocean_proximity         0\n",
+       "median_house_value      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['NEAR BAY', '<1H OCEAN', 'INLAND', 'NEAR OCEAN', 'ISLAND'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing['ocean_proximity'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "537.8705525375618"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(housing['total_bedrooms'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing=housing.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(20433, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(housing.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#housing['total_bedrooms'] = housing['total_bedrooms'].fillna(np.mean(housing['total_bedrooms']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "longitude             0\n",
+       "latitude              0\n",
+       "housing_median_age    0\n",
+       "total_rooms           0\n",
+       "total_bedrooms        0\n",
+       "population            0\n",
+       "households            0\n",
+       "median_income         0\n",
+       "ocean_proximity       0\n",
+       "median_house_value    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_var =housing.dtypes.loc[housing.dtypes == 'object'].index\n",
+    "le =LabelEncoder()\n",
+    "for var in cat_var:\n",
+    "    housing[var] = le.fit_transform(housing[var])"
+   ]
+  },
+  {
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-122.23</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>41</td>\n",
+       "      <td>880</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>322</td>\n",
+       "      <td>126</td>\n",
+       "      <td>8.3252</td>\n",
+       "      <td>3</td>\n",
+       "      <td>452600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-122.22</td>\n",
+       "      <td>37.86</td>\n",
+       "      <td>21</td>\n",
+       "      <td>7099</td>\n",
+       "      <td>1106.0</td>\n",
+       "      <td>2401</td>\n",
+       "      <td>1138</td>\n",
+       "      <td>8.3014</td>\n",
+       "      <td>3</td>\n",
+       "      <td>358500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-122.24</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52</td>\n",
+       "      <td>1467</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>496</td>\n",
+       "      <td>177</td>\n",
+       "      <td>7.2574</td>\n",
+       "      <td>3</td>\n",
+       "      <td>352100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52</td>\n",
+       "      <td>1274</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>558</td>\n",
+       "      <td>219</td>\n",
+       "      <td>5.6431</td>\n",
+       "      <td>3</td>\n",
+       "      <td>341300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52</td>\n",
+       "      <td>1627</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>565</td>\n",
+       "      <td>259</td>\n",
+       "      <td>3.8462</td>\n",
+       "      <td>3</td>\n",
+       "      <td>342200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52</td>\n",
+       "      <td>919</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>413</td>\n",
+       "      <td>193</td>\n",
+       "      <td>4.0368</td>\n",
+       "      <td>3</td>\n",
+       "      <td>269700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.84</td>\n",
+       "      <td>52</td>\n",
+       "      <td>2535</td>\n",
+       "      <td>489.0</td>\n",
+       "      <td>1094</td>\n",
+       "      <td>514</td>\n",
+       "      <td>3.6591</td>\n",
+       "      <td>3</td>\n",
+       "      <td>299200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.84</td>\n",
+       "      <td>52</td>\n",
+       "      <td>3104</td>\n",
+       "      <td>687.0</td>\n",
+       "      <td>1157</td>\n",
+       "      <td>647</td>\n",
+       "      <td>3.1200</td>\n",
+       "      <td>3</td>\n",
+       "      <td>241400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-122.26</td>\n",
+       "      <td>37.84</td>\n",
+       "      <td>42</td>\n",
+       "      <td>2555</td>\n",
+       "      <td>665.0</td>\n",
+       "      <td>1206</td>\n",
+       "      <td>595</td>\n",
+       "      <td>2.0804</td>\n",
+       "      <td>3</td>\n",
+       "      <td>226700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.84</td>\n",
+       "      <td>52</td>\n",
+       "      <td>3549</td>\n",
+       "      <td>707.0</td>\n",
+       "      <td>1551</td>\n",
+       "      <td>714</td>\n",
+       "      <td>3.6912</td>\n",
+       "      <td>3</td>\n",
+       "      <td>261100</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "0    -122.23     37.88                  41          880           129.0   \n",
+       "1    -122.22     37.86                  21         7099          1106.0   \n",
+       "2    -122.24     37.85                  52         1467           190.0   \n",
+       "3    -122.25     37.85                  52         1274           235.0   \n",
+       "4    -122.25     37.85                  52         1627           280.0   \n",
+       "5    -122.25     37.85                  52          919           213.0   \n",
+       "6    -122.25     37.84                  52         2535           489.0   \n",
+       "7    -122.25     37.84                  52         3104           687.0   \n",
+       "8    -122.26     37.84                  42         2555           665.0   \n",
+       "9    -122.25     37.84                  52         3549           707.0   \n",
+       "\n",
+       "   population  households  median_income  ocean_proximity  median_house_value  \n",
+       "0         322         126         8.3252                3              452600  \n",
+       "1        2401        1138         8.3014                3              358500  \n",
+       "2         496         177         7.2574                3              352100  \n",
+       "3         558         219         5.6431                3              341300  \n",
+       "4         565         259         3.8462                3              342200  \n",
+       "5         413         193         4.0368                3              269700  \n",
+       "6        1094         514         3.6591                3              299200  \n",
+       "7        1157         647         3.1200                3              241400  \n",
+       "8        1206         595         2.0804                3              226700  \n",
+       "9        1551         714         3.6912                3              261100  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = housing.drop(['median_house_value', 'ocean_proximity'], axis=1)\n",
+    "y = housing['median_house_value']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split (X, y, test_size = 0.2, \n",
+    "                                                     random_state = 45)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
+       "         normalize=False)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Linear Regression\n",
+    "lin = LinearRegression()\n",
+    "lin.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "70166.3884466549\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = lin.predict(X_test)\n",
+    "rmse=(np.sqrt(mean_squared_error(y_test, prediction)))\n",
+    "print(rmse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeRegressor(criterion='mse', max_depth=9, max_features=None,\n",
+       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
+       "           min_impurity_split=None, min_samples_leaf=1,\n",
+       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
+       "           presort=False, random_state=None, splitter='best')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Decision Tree Regression\n",
+    "dtree_reg = DecisionTreeRegressor(max_depth=9)\n",
+    "dtree_reg.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Root Mean Squared error = 62361.731688194246\n",
+      "Accuracy = 70.47396628112273\n",
+      "R2 score = 0.7047396628112272\n"
+     ]
+    }
+   ],
+   "source": [
+    "dt_prediction = dtree_reg.predict(X_test)\n",
+    "print(\"Root Mean Squared error = \" + str(np.sqrt(mean_squared_error(y_test, dt_prediction))))\n",
+    "dt_accuracy = dtree_reg.score(X_test,y_test)\n",
+    "print(\"Accuracy = \" + str(dt_accuracy*100))\n",
+    "print(\"R2 score = \" + str(r2_score(y_test,dt_prediction)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
+       "           max_features='auto', max_leaf_nodes=None,\n",
+       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "           min_samples_leaf=1, min_samples_split=2,\n",
+       "           min_weight_fraction_leaf=0.0, n_estimators=30, n_jobs=None,\n",
+       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Random Forest Regression\n",
+    "rf = RandomForestRegressor(30)\n",
+    "rf.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Root Mean Squared Error = 49729.7712253532\n",
+      "Accuracy = 81.22405945795688\n",
+      "R2 score = 0.8122405945795689\n"
+     ]
+    }
+   ],
+   "source": [
+    "rf_prediction = rf.predict(X_test)\n",
+    "print(\"Root Mean Squared Error = \" + str(np.sqrt(mean_squared_error(y_test,rf_prediction))))\n",
+    "rf_accuracy = rf.score(X_test,y_test)\n",
+    "print(\"Accuracy = \" + str(rf_accuracy*100))\n",
+    "print(\"R2 score = \" + str(r2_score(y_test,rf_prediction)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Bonus Exercise\n",
+    "x1 = housing.iloc[:, 7].values\n",
+    "y1 = housing.iloc[:, 9].values\n",
+    "#Spliting the dataset and Reshapping the data\n",
+    "xtrain, xtest, ytrain, ytest = train_test_split(x1, y1, test_size= 0.2, random_state=2)\n",
+    "xtrain= xtrain.reshape(-1, 1)\n",
+    "ytrain= ytrain.reshape(-1, 1)\n",
+    "xtest = xtest.reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
+       "         normalize=False)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#linear Regression\n",
+    "rg = LinearRegression()\n",
+    "rg.fit(xtrain,ytrain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Root Mean Square Error = 83723.69888227055\n",
+      "Accuracy = 45.339728251392316\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = rg.predict(xtest)\n",
+    "rms1 = np.sqrt(mean_squared_error(ytest,pred))\n",
+    "print(\"Root Mean Square Error = \" + str(rms1))\n",
+    "accuracy1 = rg.score(xtest,ytest)\n",
+    "print(\"Accuracy = \" + str(accuracy1*100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plot\n",
+    "plt.scatter(xtrain, ytrain)\n",
+    "plt.plot(xtest, pred, color='red')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Predicted median_house_value')"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(ytest, pred)\n",
+    "plt.scatter(ytrain, rg.predict(xtrain))\n",
+    "plt.xlabel(\"Actual median_house_value\")\n",
+    "plt.ylabel(\"Predicted median_house_value\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}