RandomForestRegressor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0      int64\n",
       "crim          float64\n",
       "zn            float64\n",
       "indus         float64\n",
       "chas            int64\n",
       "nox           float64\n",
       "rm            float64\n",
       "age           float64\n",
       "dis           float64\n",
       "rad             int64\n",
       "tax             int64\n",
       "ptratio       float64\n",
       "black         float64\n",
       "lstat         float64\n",
       "medv          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Boston.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['Unnamed: 0'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "crim       0\n",
       "zn         0\n",
       "indus      0\n",
       "chas       0\n",
       "nox        0\n",
       "rm         0\n",
       "age        0\n",
       "dis        0\n",
       "rad        0\n",
       "tax        0\n",
       "ptratio    0\n",
       "black      0\n",
       "lstat      0\n",
       "medv       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "crim       float64\n",
       "zn         float64\n",
       "indus      float64\n",
       "chas         int64\n",
       "nox        float64\n",
       "rm         float64\n",
       "age        float64\n",
       "dis        float64\n",
       "rad          int64\n",
       "tax          int64\n",
       "ptratio    float64\n",
       "black      float64\n",
       "lstat      float64\n",
       "medv       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = data.iloc[:, :-1].values\n",
    "y = data.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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=100, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfr = RandomForestRegressor(n_estimators = 100)\n",
    "rfr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = rfr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_log_error\n",
    "s = mean_squared_log_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97603874274774416"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = 1 - s\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dfWKK9Kc/HdQHPzie/AHhSk8TK5dThvDEKNDocIKiHgVSlxkAcsbx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\nIM8IXwAA8ozwBQAgz/4/8S79YpWV4GoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_pred, y_test, c = 'red')\n",
    "plt.plot(rfr.predict(X_test), y_pred, c = 'b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}