Data Preprocessing Data Section 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, 3].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Taking Care of Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imputer = imputer.fit(X[:, 1:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X[:, 1:3] = imputer.transform(X[:, 1:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['France', 44.0, 72000.0],\n",
       "       ['Spain', 27.0, 48000.0],\n",
       "       ['Germany', 30.0, 54000.0],\n",
       "       ['Spain', 38.0, 61000.0],\n",
       "       ['Germany', 40.0, 63777.77777777778],\n",
       "       ['France', 35.0, 58000.0],\n",
       "       ['Spain', 38.77777777777778, 52000.0],\n",
       "       ['France', 48.0, 79000.0],\n",
       "       ['Germany', 50.0, 83000.0],\n",
       "       ['France', 37.0, 67000.0]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labelencoder_X = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X[:, 0] = labelencoder_X.fit_transform(X[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "onehotencoder = OneHotEncoder(categorical_features = [0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = onehotencoder.fit_transform(X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          4.40000000e+01,   7.20000000e+04],\n",
       "       [  0.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "          2.70000000e+01,   4.80000000e+04],\n",
       "       [  0.00000000e+00,   1.00000000e+00,   0.00000000e+00,\n",
       "          3.00000000e+01,   5.40000000e+04],\n",
       "       [  0.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "          3.80000000e+01,   6.10000000e+04],\n",
       "       [  0.00000000e+00,   1.00000000e+00,   0.00000000e+00,\n",
       "          4.00000000e+01,   6.37777778e+04],\n",
       "       [  1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          3.50000000e+01,   5.80000000e+04],\n",
       "       [  0.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "          3.87777778e+01,   5.20000000e+04],\n",
       "       [  1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          4.80000000e+01,   7.90000000e+04],\n",
       "       [  0.00000000e+00,   1.00000000e+00,   0.00000000e+00,\n",
       "          5.00000000e+01,   8.30000000e+04],\n",
       "       [  1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          3.70000000e+01,   6.70000000e+04]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labelencoder_y = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = labelencoder_y.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 1, 1, 0, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning by Splitting into the Train Test Split Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sc_X = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = sc_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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