02. Regression/5. Decision Tree Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor = DecisionTreeRegressor(random_state=0).fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjQAAAFkCAYAAAA37aFpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmPEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9921eb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y)\n",
    "X_grid = np.arange(min(X.values), max(X.values), 0.1).reshape(-1, 1)\n",
    "plt.plot(X_grid, regressor.predict(X_grid), color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree regression will split according to features and assign an average value to a particular range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": 1
}