02. Regression/6. Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Trees tend to overfit the model. What random forest does is creates many different trees with random variables, and the end outcome is averaged across all the trees. This avoids overfitting and gives an accurate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Position</th>\n",
       "      <th>Level</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Junior Consultant</td>\n",
       "      <td>2</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Senior Consultant</td>\n",
       "      <td>3</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Country Manager</td>\n",
       "      <td>5</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Position  Level  Salary\n",
       "0   Business Analyst      1   45000\n",
       "1  Junior Consultant      2   50000\n",
       "2  Senior Consultant      3   60000\n",
       "3            Manager      4   80000\n",
       "4    Country Manager      5  110000"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('Position_Salaries.csv')\n",
    "X = df.iloc[:, 1:2]   # Using 1:2 as indices will give us np array of dim (10, 1)\n",
    "y = df.iloc[:, 2]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from  sklearn.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators=1000, random_state=0).fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lXVx5ojARkfKhMMkbhYmIlIV1ze2s3fI6b3XDzGWbWdfcXuwpDSkKExEZ8tY1t7N07Ta6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\n/pSuM0N0lwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b2cc198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y)\n",
    "X_grid = np.arange(min(X.values), max(X.values), 0.01).reshape(-1, 1)\n",
    "plt.plot(X_grid, regressor.predict(X_grid), color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}