Python/machine_learning/Random Forest Regression/Random Forest Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv('Position_Salaries.csv')\n",
    "X = dataset.iloc[:, 1:2].values\n",
    "y = dataset.iloc[:, 2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=300, n_jobs=1, oob_score=False, random_state=0,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Random Forest Regression to the dataset\n",
    "regressor = RandomForestRegressor(n_estimators = 300, random_state = 0)\n",
    "regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Predicting a new result\n",
    "y_pred = regressor.predict(6.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 160333.33333333]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaEAAAEWCAYAAADPZygPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntgHuTA9KrcPMzApMnNi
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa760ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Random Forest Regression results (higher resolution)\n",
    "X_grid = np.arange(min(X), max(X), 0.01)\n",
    "X_grid = X_grid.reshape((len(X_grid), 1))\n",
    "plt.scatter(X, y, color = 'red')\n",
    "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
    "plt.title('Truth or Bluff (Random Forest Regression)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}