01. Data Pre-Processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing the Data\n",
    "1. Quality of data is important to train the model\n",
    "2. Source – Government databases, professional or company data sources(twitter), your company, etc\n",
    "3. Data will never be in the format you need – Pandas Dataframe for reformatting\n",
    "4. Columns to remove – No values, duplicate(correlated column, e.g: house size in ft and metres)\n",
    "5. Preprocessing refers to transformation before feeding to machine learning\n",
    "6. Learning algorithms understands only number, converting text image to number is required\n",
    "7. Unscaled or unstandardized data have might have unacceptable prediction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Types of Data Preprocessing\n",
    "1. Checking for Null Values\n",
    "2. Correlated Feature Check\n",
    "3. Data Molding (Label Encoding)\n",
    "4. Splitting the Data\n",
    "5. Impute Missing Values\n",
    "6. Data Standardization (Feature Scaling)\n",
    "7. Label Encoding\n",
    "8. One-Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_preg</th>\n",
       "      <th>glucose_conc</th>\n",
       "      <th>diastolic_bp</th>\n",
       "      <th>thickness</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diab_pred</th>\n",
       "      <th>age</th>\n",
       "      <th>skin</th>\n",
       "      <th>diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1.3790</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>1.1426</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0.9062</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1.3790</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1.2608</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1.7730</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_preg  glucose_conc  diastolic_bp  thickness  insulin   bmi  diab_pred  \\\n",
       "0         6           148            72         35        0  33.6      0.627   \n",
       "1         1            85            66         29        0  26.6      0.351   \n",
       "2         8           183            64          0        0  23.3      0.672   \n",
       "3         1            89            66         23       94  28.1      0.167   \n",
       "4         0           137            40         35      168  43.1      2.288   \n",
       "5         5           116            74          0        0  25.6      0.201   \n",
       "6         3            78            50         32       88  31.0      0.248   \n",
       "7        10           115             0          0        0  35.3      0.134   \n",
       "8         2           197            70         45      543  30.5      0.158   \n",
       "9         8           125            96          0        0   0.0      0.232   \n",
       "\n",
       "   age    skin  diabetes  \n",
       "0   50  1.3790      True  \n",
       "1   31  1.1426     False  \n",
       "2   32  0.0000      True  \n",
       "3   21  0.9062     False  \n",
       "4   33  1.3790      True  \n",
       "5   30  0.0000     False  \n",
       "6   26  1.2608      True  \n",
       "7   29  0.0000     False  \n",
       "8   53  1.7730      True  \n",
       "9   54  0.0000      True  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd                 # pandas is a dataframe library\n",
    "import matplotlib.pyplot as plt      # matplotlib.pyplot plots data\n",
    "\n",
    "%matplotlib inline\n",
    "#Loading and Reviewing the Data\n",
    "df = pd.read_csv(\"Data/Data_Pre_Processing/pima-data.csv\")\n",
    "#print(df.shape)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlated Feature Check\n",
    "Helper function that displays correlation by color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_corr(df, size=11):\n",
    "    \"\"\"\n",
    "    Function plots a graphical correlation matrix for each pair of columns in the dataframe.\n",
    "\n",
    "    Input:\n",
    "        df: pandas DataFrame\n",
    "        size: vertical and horizontal size of the plot\n",
    "\n",
    "    Displays:\n",
    "        matrix of correlation between columns.\n",
    "    \"\"\"\n",
    "\n",
    "    corr = df.corr()    # data frame correlation function\n",
    "    fig, ax = plt.subplots(figsize=(size, size))#The subplots command in the background will call plt.figure(), and any keywords will be passed along\n",
    "    ax.matshow(corr)   # heatmap with matshow, color code the rectangles by correlation value\n",
    "    plt.xticks(range(len(corr.columns)), corr.columns)  # draw x tick marks\n",
    "    plt.yticks(range(len(corr.columns)), corr.columns)  # draw y tick marks\n",
    "    \n",
    "plot_corr(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_preg</th>\n",
       "      <th>glucose_conc</th>\n",
       "      <th>diastolic_bp</th>\n",
       "      <th>thickness</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diab_pred</th>\n",
       "      <th>age</th>\n",
       "      <th>skin</th>\n",
       "      <th>diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>num_preg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glucose_conc</th>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diastolic_bp</th>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thickness</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insulin</th>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.130548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diab_pred</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skin</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.466581</td>\n",
       "      <td>0.065068</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              num_preg  glucose_conc  diastolic_bp  thickness   insulin  \\\n",
       "num_preg      1.000000      0.129459      0.141282  -0.081672 -0.073535   \n",
       "glucose_conc  0.129459      1.000000      0.152590   0.057328  0.331357   \n",
       "diastolic_bp  0.141282      0.152590      1.000000   0.207371  0.088933   \n",
       "thickness    -0.081672      0.057328      0.207371   1.000000  0.436783   \n",
       "insulin      -0.073535      0.331357      0.088933   0.436783  1.000000   \n",
       "bmi           0.017683      0.221071      0.281805   0.392573  0.197859   \n",
       "diab_pred    -0.033523      0.137337      0.041265   0.183928  0.185071   \n",
       "age           0.544341      0.263514      0.239528  -0.113970 -0.042163   \n",
       "skin         -0.081672      0.057328      0.207371   1.000000  0.436783   \n",
       "diabetes      0.221898      0.466581      0.065068   0.074752  0.130548   \n",
       "\n",
       "                   bmi  diab_pred       age      skin  diabetes  \n",
       "num_preg      0.017683  -0.033523  0.544341 -0.081672  0.221898  \n",
       "glucose_conc  0.221071   0.137337  0.263514  0.057328  0.466581  \n",
       "diastolic_bp  0.281805   0.041265  0.239528  0.207371  0.065068  \n",
       "thickness     0.392573   0.183928 -0.113970  1.000000  0.074752  \n",
       "insulin       0.197859   0.185071 -0.042163  0.436783  0.130548  \n",
       "bmi           1.000000   0.140647  0.036242  0.392573  0.292695  \n",
       "diab_pred     0.140647   1.000000  0.033561  0.183928  0.173844  \n",
       "age           0.036242   0.033561  1.000000 -0.113970  0.238356  \n",
       "skin          0.392573   0.183928 -0.113970  1.000000  0.074752  \n",
       "diabetes      0.292695   0.173844  0.238356  0.074752  1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_preg</th>\n",
       "      <th>glucose_conc</th>\n",
       "      <th>diastolic_bp</th>\n",
       "      <th>thickness</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diab_pred</th>\n",
       "      <th>age</th>\n",
       "      <th>skin</th>\n",
       "      <th>diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1.3790</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>1.1426</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0.9062</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1.3790</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_preg  glucose_conc  diastolic_bp  thickness  insulin   bmi  diab_pred  \\\n",
       "0         6           148            72         35        0  33.6      0.627   \n",
       "1         1            85            66         29        0  26.6      0.351   \n",
       "2         8           183            64          0        0  23.3      0.672   \n",
       "3         1            89            66         23       94  28.1      0.167   \n",
       "4         0           137            40         35      168  43.1      2.288   \n",
       "\n",
       "   age    skin  diabetes  \n",
       "0   50  1.3790      True  \n",
       "1   31  1.1426     False  \n",
       "2   32  0.0000      True  \n",
       "3   21  0.9062     False  \n",
       "4   33  1.3790      True  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The skin and thickness columns are correlated 1 to 1. Dropping the skin column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['skin']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for additional correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_corr(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Molding(Label Encoding)\n",
    "1. Adjusting Data Types - Inspect data types to see if there are any issues. Data should be numeric.\n",
    "2. If required create new columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_preg</th>\n",
       "      <th>glucose_conc</th>\n",
       "      <th>diastolic_bp</th>\n",
       "      <th>thickness</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>diab_pred</th>\n",
       "      <th>age</th>\n",
       "      <th>diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_preg  glucose_conc  diastolic_bp  thickness  insulin   bmi  diab_pred  \\\n",
       "0         6           148            72         35        0  33.6      0.627   \n",
       "1         1            85            66         29        0  26.6      0.351   \n",
       "2         8           183            64          0        0  23.3      0.672   \n",
       "3         1            89            66         23       94  28.1      0.167   \n",
       "4         0           137            40         35      168  43.1      2.288   \n",
       "\n",
       "   age  diabetes  \n",
       "0   50         1  \n",
       "1   31         0  \n",
       "2   32         1  \n",
       "3   21         0  \n",
       "4   33         1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_map = {True : 1, False : 0}\n",
    "df['diabetes'] = df['diabetes'].map(diabetes_map)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the Data\n",
    "* Less training data -> your parameter estimates have greater variance\n",
    "* Less testing data -> your performance statistic will have greater variance\n",
    "\n",
    "### Divide data such that neither variance is too high\n",
    "\n",
    "* Less data -> chances of no satisfactory variance\n",
    "* More data -> split doesn't really matter\n",
    "\n",
    "X = feature, independent, predictor\n",
    "Y = predicted, dependent\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spliting the data¶\n",
    "\n",
    "70% for training, 30% for testing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69.92% in training set\n",
      "30.08% in test set\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "feature_col_names = ['num_preg', 'glucose_conc', 'diastolic_bp', 'thickness', 'insulin', 'bmi', 'diab_pred', 'age']\n",
    "predicted_class_names = ['diabetes']\n",
    "\n",
    "X = df[feature_col_names].values     # predictor feature columns (8 X m)\n",
    "y = df[predicted_class_names].values # predicted class (1=true, 0=false) column (1 X m)\n",
    "split_test_size = 0.30\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=split_test_size, random_state=42) \n",
    "                            # test_size = 0.3 is 30%, 42 is the answer to everything\n",
    "#We check to ensure we have the the desired 70% train, 30% test split of the data\n",
    "print(\"{0:0.2f}% in training set\".format((float(len(X_train))/float(len(df.index)))*100))\n",
    "print(\"{0:0.2f}% in test set\".format((float(len(X_test))/float(len(df.index)))*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Impute Missing Values\n",
    "\n",
    "## Missing Data\n",
    "1. Ignore\n",
    "2. Drop rows\n",
    "3. Replace values (Impute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# rows in dataframe 768\n",
      "# rows missing glucose_conc: 5\n",
      "# rows missing diastolic_bp: 35\n",
      "# rows missing thickness: 227\n",
      "# rows missing insulin: 374\n",
      "# rows missing bmi: 11\n",
      "# rows missing diab_pred: 0\n",
      "# rows missing age: 0\n"
     ]
    }
   ],
   "source": [
    "#Are there 0 values possible?\n",
    "#How many rows have have unexpected 0 values?\n",
    "print(\"# rows in dataframe {0}\".format(len(df)))\n",
    "print(\"# rows missing glucose_conc: {0}\".format(len(df.loc[df['glucose_conc'] == 0])))\n",
    "print(\"# rows missing diastolic_bp: {0}\".format(len(df.loc[df['diastolic_bp'] == 0])))\n",
    "print(\"# rows missing thickness: {0}\".format(len(df.loc[df['thickness'] == 0])))\n",
    "print(\"# rows missing insulin: {0}\".format(len(df.loc[df['insulin'] == 0])))\n",
    "print(\"# rows missing bmi: {0}\".format(len(df.loc[df['bmi'] == 0])))\n",
    "print(\"# rows missing diab_pred: {0}\".format(len(df.loc[df['diab_pred'] == 0])))\n",
    "print(\"# rows missing age: {0}\".format(len(df.loc[df['age'] == 0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Impute with the mean\n",
    "from sklearn.impute import SimpleImputer \n",
    "\n",
    "#Impute with mean all 0 readings\n",
    "fill_0 = SimpleImputer(missing_values=0, strategy=\"mean\")\n",
    "\n",
    "X_train = fill_0.fit_transform(X_train)\n",
    "X_test = fill_0.fit_transform(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Standardization (Feature Scaling)\n",
    "\n",
    "Data standardization is the process of rescaling one or more attributes so that they have a mean value of 0 and a standard deviation of 1. Standardization assumes that your data has a Gaussian (bell curve) distribution.  \n",
    "\n",
    "Apply scaling on the test scores to express them in terms of z-score  \n",
    "Z-score is the expression of a value in terms of the number of standard deviations from the mean  \n",
    "The effect is to give a score which is relative to the the distribution of values for that column  \n",
    "\n",
    "z = $\\frac {(x – μ)}{\\sigma}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>group E</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>79</td>\n",
       "      <td>75</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>56</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>male</td>\n",
       "      <td>group D</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>86</td>\n",
       "      <td>68</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>group A</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>68</td>\n",
       "      <td>78</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>group D</td>\n",
       "      <td>high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>49</td>\n",
       "      <td>68</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0    male        group E          associate's degree      standard   \n",
       "1  female        group C          associate's degree  free/reduced   \n",
       "2    male        group D           bachelor's degree      standard   \n",
       "3  female        group A           bachelor's degree      standard   \n",
       "4  female        group D                 high school  free/reduced   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \n",
       "0               completed          79             75             81  \n",
       "1                    none          56             65             64  \n",
       "2                    none          86             68             74  \n",
       "3                    none          68             78             76  \n",
       "4                    none          49             68             61  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "exam_data = pd.read_csv('Data/Data_Pre_Processing/exams.csv', quotechar='\"')\n",
    "exam_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>group E</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>0.994557</td>\n",
       "      <td>0.574138</td>\n",
       "      <td>1.049901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>-0.646391</td>\n",
       "      <td>-0.169564</td>\n",
       "      <td>-0.178476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>male</td>\n",
       "      <td>group D</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>1.493976</td>\n",
       "      <td>0.053547</td>\n",
       "      <td>0.544099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>group A</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>0.209756</td>\n",
       "      <td>0.797248</td>\n",
       "      <td>0.688613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>group D</td>\n",
       "      <td>high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>-1.145810</td>\n",
       "      <td>0.053547</td>\n",
       "      <td>-0.395248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0    male        group E          associate's degree      standard   \n",
       "1  female        group C          associate's degree  free/reduced   \n",
       "2    male        group D           bachelor's degree      standard   \n",
       "3  female        group A           bachelor's degree      standard   \n",
       "4  female        group D                 high school  free/reduced   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \n",
       "0               completed    0.994557       0.574138       1.049901  \n",
       "1                    none   -0.646391      -0.169564      -0.178476  \n",
       "2                    none    1.493976       0.053547       0.544099  \n",
       "3                    none    0.209756       0.797248       0.688613  \n",
       "4                    none   -1.145810       0.053547      -0.395248  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "exam_data[['math score']] = exam_data[['math score']].astype(float)\n",
    "exam_data[['reading score']] = exam_data[['reading score']].astype(float)\n",
    "exam_data[['writing score']] = exam_data[['writing score']].astype(float)\n",
    "exam_data[['math score']] = preprocessing.scale(exam_data[['math score']])\n",
    "exam_data[['reading score']] = preprocessing.scale(exam_data[['reading score']])\n",
    "exam_data[['writing score']] = preprocessing.scale(exam_data[['writing score']])\n",
    "exam_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plot = exam_data[['math score', 'reading score', 'writing score']].copy()\n",
    "df_plot.plot.kde()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Encoding:\n",
    "Convert text values to numbers. These can be used in the following situations:\n",
    "* There are only two values for a column in your data. The values will then become 0/1 - effectively a binary representation\n",
    "* The values have relationship with each other where comparisons are meaningful (e.g. low<medium<high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>group E</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>0.994557</td>\n",
       "      <td>0.574138</td>\n",
       "      <td>1.049901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>group C</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>-0.646391</td>\n",
       "      <td>-0.169564</td>\n",
       "      <td>-0.178476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>group D</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>1.493976</td>\n",
       "      <td>0.053547</td>\n",
       "      <td>0.544099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>group A</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>0.209756</td>\n",
       "      <td>0.797248</td>\n",
       "      <td>0.688613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>group D</td>\n",
       "      <td>high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>-1.145810</td>\n",
       "      <td>0.053547</td>\n",
       "      <td>-0.395248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0       1        group E          associate's degree      standard   \n",
       "1       0        group C          associate's degree  free/reduced   \n",
       "2       1        group D           bachelor's degree      standard   \n",
       "3       0        group A           bachelor's degree      standard   \n",
       "4       0        group D                 high school  free/reduced   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \n",
       "0               completed    0.994557       0.574138       1.049901  \n",
       "1                    none   -0.646391      -0.169564      -0.178476  \n",
       "2                    none    1.493976       0.053547       0.544099  \n",
       "3                    none    0.209756       0.797248       0.688613  \n",
       "4                    none   -1.145810       0.053547      -0.395248  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = preprocessing.LabelEncoder()\n",
    "exam_data['gender'] = le.fit_transform(exam_data['gender'].astype(str))\n",
    "exam_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['female' 'male']\n"
     ]
    }
   ],
   "source": [
    "print(le.classes_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Hot Encoding:\n",
    "* Use when there is no meaningful comparison between values in the column\n",
    "* Creates a new column for each unique value for the specified feature in the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "      <th>race/ethnicity_group A</th>\n",
       "      <th>race/ethnicity_group B</th>\n",
       "      <th>race/ethnicity_group C</th>\n",
       "      <th>race/ethnicity_group D</th>\n",
       "      <th>race/ethnicity_group E</th>\n",
       "      <th>parental level of education_associate's degree</th>\n",
       "      <th>parental level of education_bachelor's degree</th>\n",
       "      <th>parental level of education_high school</th>\n",
       "      <th>parental level of education_master's degree</th>\n",
       "      <th>parental level of education_some college</th>\n",
       "      <th>parental level of education_some high school</th>\n",
       "      <th>lunch_free/reduced</th>\n",
       "      <th>lunch_standard</th>\n",
       "      <th>test preparation course_completed</th>\n",
       "      <th>test preparation course_none</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.994557</td>\n",
       "      <td>0.574138</td>\n",
       "      <td>1.049901</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.646391</td>\n",
       "      <td>-0.169564</td>\n",
       "      <td>-0.178476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.493976</td>\n",
       "      <td>0.053547</td>\n",
       "      <td>0.544099</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.209756</td>\n",
       "      <td>0.797248</td>\n",
       "      <td>0.688613</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.145810</td>\n",
       "      <td>0.053547</td>\n",
       "      <td>-0.395248</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  math score  reading score  writing score  race/ethnicity_group A  \\\n",
       "0       1    0.994557       0.574138       1.049901                       0   \n",
       "1       0   -0.646391      -0.169564      -0.178476                       0   \n",
       "2       1    1.493976       0.053547       0.544099                       0   \n",
       "3       0    0.209756       0.797248       0.688613                       1   \n",
       "4       0   -1.145810       0.053547      -0.395248                       0   \n",
       "\n",
       "   race/ethnicity_group B  race/ethnicity_group C  race/ethnicity_group D  \\\n",
       "0                       0                       0                       0   \n",
       "1                       0                       1                       0   \n",
       "2                       0                       0                       1   \n",
       "3                       0                       0                       0   \n",
       "4                       0                       0                       1   \n",
       "\n",
       "   race/ethnicity_group E  parental level of education_associate's degree  \\\n",
       "0                       1                                               1   \n",
       "1                       0                                               1   \n",
       "2                       0                                               0   \n",
       "3                       0                                               0   \n",
       "4                       0                                               0   \n",
       "\n",
       "   parental level of education_bachelor's degree  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              1   \n",
       "3                                              1   \n",
       "4                                              0   \n",
       "\n",
       "   parental level of education_high school  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        1   \n",
       "\n",
       "   parental level of education_master's degree  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "\n",
       "   parental level of education_some college  \\\n",
       "0                                         0   \n",
       "1                                         0   \n",
       "2                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "\n",
       "   parental level of education_some high school  lunch_free/reduced  \\\n",
       "0                                             0                   0   \n",
       "1                                             0                   1   \n",
       "2                                             0                   0   \n",
       "3                                             0                   0   \n",
       "4                                             0                   1   \n",
       "\n",
       "   lunch_standard  test preparation course_completed  \\\n",
       "0               1                                  1   \n",
       "1               0                                  0   \n",
       "2               1                                  0   \n",
       "3               1                                  0   \n",
       "4               0                                  0   \n",
       "\n",
       "   test preparation course_none  \n",
       "0                             0  \n",
       "1                             1  \n",
       "2                             1  \n",
       "3                             1  \n",
       "4                             1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exam_data = pd.get_dummies(exam_data, columns=['race/ethnicity','parental level of education', 'lunch', \n",
    "                                               'test preparation course'])\n",
    "exam_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Difference between OneHot and get_dummies\n",
    "OneHotEncoder cannot process string values directly. If your nominal features are strings, then you need to first map them into integers.\n",
    "\n",
    "pandas.get_dummies is kind of the opposite. By default, it only converts string columns into one-hot representation, unless columns are specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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