{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f746e7db",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "418ea266",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6f70b0e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature=data.data\n",
    "label= data.target\n",
    "feature_names= data.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10584727",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1ed54aae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "52f4cdbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(feature, label, test_size=0.25, random_state=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e7c4b996",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "clfr= RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "10ff4b5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clfr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "51c1b6eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8091590187385096"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clfr.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26039747",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "86d6e338",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3a13358d",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bfa90062",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "28114c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.DataFrame(feature, columns=feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b2c1f4c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MedVal']= pd.DataFrame(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "570fc9e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc        0.688075\n",
       "HouseAge      0.105623\n",
       "AveRooms      0.151948\n",
       "AveBedrms    -0.046701\n",
       "Population   -0.024650\n",
       "AveOccup     -0.023737\n",
       "Latitude     -0.144160\n",
       "Longitude    -0.045967\n",
       "MedVal        1.000000\n",
       "Name: MedVal, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['MedVal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b9d79e67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AveRooms', ylabel='MedVal'>"
      ]
     },
     "execution_count": 52,
     "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": [
    "from matplotlib import pyplot as plt\n",
    "df.plot(kind='scatter', x='AveRooms', y= 'MedVal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e5b38a8",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}