Pima-prediction-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "57b7ef3d",
   "metadata": {},
   "source": [
    "# Predicting Diabetes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "728bd1fd",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c82161c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd       #pandas is a dataframe library\n",
    "import matplotlib.pyplot as plt      #matplotlib.pyplot plots the data\n",
    "import numpy as np       #numpy provides N-dim object support\n",
    "\n",
    "# Do plotting inline instead of doing it in a seperate window\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac95cfa",
   "metadata": {},
   "source": [
    "# Load and review data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f3604fb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/avira/OneDrive/Desktop/PluralSight/MachineLearningWithPython-master/Notebooks/data/pima-data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "da45e82e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fd07504c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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       "      <th>0</th>\n",
       "      <td>6</td>\n",
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       "      <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",
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       "      <th>1</th>\n",
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       "      <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",
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       "      <th>3</th>\n",
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       "      <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "21058969",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>1.8912</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0638</td>\n",
       "      <td>False</td>\n",
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       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0.9062</td>\n",
       "      <td>False</td>\n",
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       "      <th>766</th>\n",
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       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>1.2214</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     num_preg  glucose_conc  diastolic_bp  thickness  insulin   bmi  \\\n",
       "763        10           101            76         48      180  32.9   \n",
       "764         2           122            70         27        0  36.8   \n",
       "765         5           121            72         23      112  26.2   \n",
       "766         1           126            60          0        0  30.1   \n",
       "767         1            93            70         31        0  30.4   \n",
       "\n",
       "     diab_pred  age    skin  diabetes  \n",
       "763      0.171   63  1.8912     False  \n",
       "764      0.340   27  1.0638     False  \n",
       "765      0.245   30  0.9062     False  \n",
       "766      0.349   47  0.0000      True  \n",
       "767      0.315   23  1.2214     False  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e39ccf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e9ceb629",
   "metadata": {},
   "outputs": [],
   "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. Blue-cyan-yellow-red-darkred => less to more correlated\n",
    "                                           0----------------------->1\n",
    "                                           Expect a darkred line running from top left to bottom right\n",
    "    \"\"\"\n",
    "    \n",
    "    corr=df.corr()     #data from correlation function\n",
    "    fig, ax=plt.subplots(figsize=(size,size))\n",
    "    ax.matshow(corr)     #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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "744e8421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1100x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_corr(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d4b02eb2",
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c3d8654d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>num_preg</th>\n",
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       "      <td>21</td>\n",
       "      <td>0.9062</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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      ],
      "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fb6c7c82",
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['skin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "36adac6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "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      True  \n",
       "1   31     False  \n",
       "2   32      True  \n",
       "3   21     False  \n",
       "4   33      True  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4716d1c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1100x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_corr(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04ecfda7",
   "metadata": {},
   "source": [
    "# Check Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fbce62ea",
   "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>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>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>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>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>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  diabetes  \n",
       "0   50      True  \n",
       "1   31     False  \n",
       "2   32      True  \n",
       "3   21     False  \n",
       "4   33      True  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a6739fe",
   "metadata": {},
   "source": [
    "Change True to 1, False to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cd5f41b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_map = {True:1, False:0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e4c017a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['diabetes'] = df['diabetes'].map(diabetes_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9efe92c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>diabetes</th>\n",
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       "  </thead>\n",
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       "      <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",
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       "      <td>85</td>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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       "    <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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15e51c76",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19dd782c",
   "metadata": {},
   "source": [
    "# Check True or False ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f5e4c6df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of True cases:  268 (34.90%)\n",
      "Number of False cases: 500 (65.10%)\n"
     ]
    }
   ],
   "source": [
    "num_true = len(df.loc[df['diabetes'] == True])\n",
    "num_false = len(df.loc[df['diabetes'] == False])\n",
    "print(\"Number of True cases:  {0} ({1:2.2f}%)\".format(num_true, (num_true/ (num_true + num_false)) * 100))\n",
    "print(\"Number of False cases: {0} ({1:2.2f}%)\".format(num_false, (num_false/ (num_true + num_false)) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06ec0580",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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