DD_Feature_Selection.ipynb

{
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  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nilesh\\AppData\\Local\\Temp\\ipykernel_57028\\972261380.py:10: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from scipy.stats import ttest_ind, chi2_contingency\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "import warnings\n",
    "from pandas import MultiIndex, Int64Index\n",
    "warnings.filterwarnings('ignore')\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "62ff2af5",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </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",
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       "      <td>85</td>\n",
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       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
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       "      <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",
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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",
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       "      <td>35</td>\n",
       "      <td>168</td>\n",
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       "      <th>763</th>\n",
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       "      <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>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <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",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>767</th>\n",
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       "      <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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \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",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir (\"C:\\\\Users\\\\Nilesh\\\\Documents\\\\GitHub\\\\01 Diabetes Prediction\\\\\")\n",
    "df = pd.read_csv(\"Diabetes-dataset.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "230ffc8d",
   "metadata": {},
   "source": [
    "## Identifying the dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "17dc76d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "00889834",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = df.columns\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bd94359c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
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       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
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       "      <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",
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      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e57628b",
   "metadata": {},
   "source": [
    "## Variable identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "77c8f855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   int64\n",
       "Glucose                       int64\n",
       "BloodPressure                 int64\n",
       "SkinThickness                 int64\n",
       "Insulin                       int64\n",
       "BMI                         float64\n",
       "DiabetesPedigreeFunction    float64\n",
       "Age                           int64\n",
       "Outcome                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c0556b4",
   "metadata": {},
   "source": [
    "Here, all variables are contineous in nature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea1b0486",
   "metadata": {},
   "source": [
    "## Univarient Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "587da602",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
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       "      <th>Outcome</th>\n",
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       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
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       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1b3eccb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Outcome'}, ylabel='Frequency'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x540 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [13.50, 7.50]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "figure, axis = plt.subplots(3, 3)\n",
    "  \n",
    "df['Pregnancies'].plot.hist(title=\"Pregnancies\",ax=axis[0][0],color=\"MEDIUMVIOLETRED\")\n",
    "df['Glucose'].plot.hist(title=\"Glucose\",ax=axis[0][1],color=\"INDIGO\")\n",
    "df['BloodPressure'].plot.hist(title=\"Blood Pressure\",ax=axis[0][2],color=\"MEDIUMVIOLETRED\")\n",
    "df['SkinThickness'].plot.hist(title=\"Skin Thickness\",ax=axis[1][0],color=\"INDIGO\")\n",
    "df['Insulin'].plot.hist(title=\"Insulin\",ax=axis[1][1],color=\"MEDIUMVIOLETRED\")\n",
    "df['BMI'].plot.hist(title=\"BMI\",ax=axis[1][2],color=\"INDIGO\")\n",
    "df['DiabetesPedigreeFunction'].plot.hist(title=\"Diabetes Pedigree Function\",ax=axis[2][0],color=\"MEDIUMVIOLETRED\")\n",
    "df['Age'].plot.hist(title=\"Age\",ax=axis[2][1],color=\"INDIGO\")\n",
    "df['Outcome'].plot.hist(title=\"Outcome\",ax=axis[2][2],color=\"MEDIUMVIOLETRED\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6e736408",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Outcome'}>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x540 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [13.50, 7.50]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "figure, axis = plt.subplots(3, 3)\n",
    "  \n",
    "df['Pregnancies'].plot.box(title=\"Pregnancies\",ax=axis[0][0],color=\"MEDIUMVIOLETRED\")\n",
    "df['Glucose'].plot.box(title=\"Glucose\",ax=axis[0][1],color=\"INDIGO\")\n",
    "df['BloodPressure'].plot.box(title=\"Blood Pressure\",ax=axis[0][2],color=\"MEDIUMVIOLETRED\")\n",
    "df['SkinThickness'].plot.box(title=\"Skin Thickness\",ax=axis[1][0],color=\"INDIGO\")\n",
    "df['Insulin'].plot.box(title=\"Insulin\",ax=axis[1][1],color=\"MEDIUMVIOLETRED\")\n",
    "df['BMI'].plot.box(title=\"BMI\",ax=axis[1][2],color=\"INDIGO\")\n",
    "df['DiabetesPedigreeFunction'].plot.box(title=\"Diabetes Pedigree Function\",ax=axis[2][0],color=\"MEDIUMVIOLETRED\")\n",
    "df['Age'].plot.box(title=\"Age\",ax=axis[2][1],color=\"INDIGO\")\n",
    "df['Outcome'].plot.box(title=\"Outcome\",ax=axis[2][2],color=\"MEDIUMVIOLETRED\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d19c3d8",
   "metadata": {},
   "source": [
    "## Bivarient Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dddd6127",
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</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>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</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>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</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>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</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>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>0.130548</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>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</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>0.173844</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>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</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>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
       "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
       "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
       "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
       "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
       "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
       "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
       "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
       "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
       "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
       "\n",
       "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
       "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
       "Glucose                   0.331357  0.221071                  0.137337   \n",
       "BloodPressure             0.088933  0.281805                  0.041265   \n",
       "SkinThickness             0.436783  0.392573                  0.183928   \n",
       "Insulin                   1.000000  0.197859                  0.185071   \n",
       "BMI                       0.197859  1.000000                  0.140647   \n",
       "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
       "Age                      -0.042163  0.036242                  0.033561   \n",
       "Outcome                   0.130548  0.292695                  0.173844   \n",
       "\n",
       "                               Age   Outcome  \n",
       "Pregnancies               0.544341  0.221898  \n",
       "Glucose                   0.263514  0.466581  \n",
       "BloodPressure             0.239528  0.065068  \n",
       "SkinThickness            -0.113970  0.074752  \n",
       "Insulin                  -0.042163  0.130548  \n",
       "BMI                       0.036242  0.292695  \n",
       "DiabetesPedigreeFunction  0.033561  0.173844  \n",
       "Age                       1.000000  0.238356  \n",
       "Outcome                   0.238356  1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "51d3cbb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x540 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataplot = sns.heatmap(df.corr(), cmap='RdPu', annot=True, center=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b08d20e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f5a2dd81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Outcome'}>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x540 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [13.50, 7.50]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "figure, axis = plt.subplots(3, 3)\n",
    "  \n",
    "df['Pregnancies'].plot.box(title=\"Pregnancies\",ax=axis[0][0],color=\"MEDIUMVIOLETRED\")\n",
    "df['Glucose'].plot.box(title=\"Glucose\",ax=axis[0][1],color=\"INDIGO\")\n",
    "df['BloodPressure'].plot.box(title=\"Blood Pressure\",ax=axis[0][2],color=\"MEDIUMVIOLETRED\")\n",
    "df['SkinThickness'].plot.box(title=\"Skin Thickness\",ax=axis[1][0],color=\"INDIGO\")\n",
    "df['Insulin'].plot.box(title=\"Insulin\",ax=axis[1][1],color=\"MEDIUMVIOLETRED\")\n",
    "df['BMI'].plot.box(title=\"BMI\",ax=axis[1][2],color=\"INDIGO\")\n",
    "df['DiabetesPedigreeFunction'].plot.box(title=\"Diabetes Pedigree Function\",ax=axis[2][0],color=\"MEDIUMVIOLETRED\")\n",
    "df['Age'].plot.box(title=\"Age\",ax=axis[2][1],color=\"INDIGO\")\n",
    "df['Outcome'].plot.box(title=\"Outcome\",ax=axis[2][2],color=\"MEDIUMVIOLETRED\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff9cd1a",
   "metadata": {},
   "source": [
    "Checking highly correlated attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fcf4ab95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=(20,8)\n",
    "f, (ax1,ax2,ax3,ax4) = plt.subplots (1,4)\n",
    "sns.boxplot ('Outcome', y = 'Glucose',data = df , ax = ax1,palette=\"RdPu\")\n",
    "sns.boxplot ('Outcome', y = 'BloodPressure',data = df , ax = ax2, palette=\"RdPu\")\n",
    "sns.boxplot ('Outcome', y = 'SkinThickness',data = df , ax = ax3, palette=\"RdPu\")\n",
    "sns.boxplot ('Outcome', y = 'Insulin',data = df , ax = ax4, palette=\"RdPu\")\n",
    "f .tight_layout()\n",
    "\n",
    "f, (ax1,ax2,ax3,ax4) = plt.subplots (1,4)\n",
    "sns.boxplot ('Outcome', y = 'BMI',data = df , ax = ax1, palette=\"RdPu\")\n",
    "sns.boxplot ('Outcome', y = 'DiabetesPedigreeFunction',data = df , ax = ax2, palette=\"RdPu\")\n",
    "sns.boxplot ('Outcome', y = 'Age',data = df , ax = ax3, palette=\"RdPu\")\n",
    "sns.boxplot ('Outcome', y = 'Pregnancies',data = df , ax = ax4, palette=\"RdPu\")\n",
    "f .tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b9fe64a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We can drop either Pregnancies or Age\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(columns)-1):\n",
    "    for j in range(i+1,len(columns)):\n",
    "        if 0.5<=df[columns[i]].corr(df[columns[j]]):\n",
    "            print(\"We can drop either \"+str(columns[i])+\" or \"+str(columns[j]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "adf162a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = columns[1:]\n",
    "df.drop(['Pregnancies'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8cf1eb25",
   "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>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>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>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>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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0        148             72             35        0  33.6   \n",
       "1         85             66             29        0  26.6   \n",
       "2        183             64              0        0  23.3   \n",
       "3         89             66             23       94  28.1   \n",
       "4        137             40             35      168  43.1   \n",
       "..       ...            ...            ...      ...   ...   \n",
       "763      101             76             48      180  32.9   \n",
       "764      122             70             27        0  36.8   \n",
       "765      121             72             23      112  26.2   \n",
       "766      126             60              0        0  30.1   \n",
       "767       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1328b7ac",
   "metadata": {},
   "source": [
    "Hence, here we are dropping Pregnancies attribute because it is highly correlated to Age attribute"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbfee4d6",
   "metadata": {},
   "source": [
    "## Missing Value Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2fa43c17",
   "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>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Glucose  BloodPressure  SkinThickness     Insulin         BMI  \\\n",
       "count  768.000000     768.000000     768.000000  768.000000  768.000000   \n",
       "mean   120.894531      69.105469      20.536458   79.799479   31.992578   \n",
       "std     31.972618      19.355807      15.952218  115.244002    7.884160   \n",
       "min      0.000000       0.000000       0.000000    0.000000    0.000000   \n",
       "25%     99.000000      62.000000       0.000000    0.000000   27.300000   \n",
       "50%    117.000000      72.000000      23.000000   30.500000   32.000000   \n",
       "75%    140.250000      80.000000      32.000000  127.250000   36.600000   \n",
       "max    199.000000     122.000000      99.000000  846.000000   67.100000   \n",
       "\n",
       "       DiabetesPedigreeFunction         Age     Outcome  \n",
       "count                768.000000  768.000000  768.000000  \n",
       "mean                   0.471876   33.240885    0.348958  \n",
       "std                    0.331329   11.760232    0.476951  \n",
       "min                    0.078000   21.000000    0.000000  \n",
       "25%                    0.243750   24.000000    0.000000  \n",
       "50%                    0.372500   29.000000    0.000000  \n",
       "75%                    0.626250   41.000000    1.000000  \n",
       "max                    2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c99e35d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "418e4b99",
   "metadata": {},
   "source": [
    "There are no missing values in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5fc286a",
   "metadata": {},
   "source": [
    "## Outliers Treatement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "843ca4e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Outcome'}>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x540 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [13.50, 7.50]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "figure, axis = plt.subplots(2, 4)\n",
    "  \n",
    "df['Glucose'].plot.box(title=\"Glucose\",ax=axis[0][0],color=\"MEDIUMVIOLETRED\")\n",
    "df['BloodPressure'].plot.box(title=\"Blood Pressure\",ax=axis[0][1],color=\"INDIGO\")\n",
    "df['SkinThickness'].plot.box(title=\"Skin Thickness\",ax=axis[0][2],color=\"MEDIUMVIOLETRED\")\n",
    "df['Insulin'].plot.box(title=\"Insulin\",ax=axis[0][3],color=\"INDIGO\")\n",
    "df['BMI'].plot.box(title=\"BMI\",ax=axis[1][0],color=\"INDIGO\")\n",
    "df['DiabetesPedigreeFunction'].plot.box(title=\"Diabetes Pedigree Function\",ax=axis[1][1],color=\"MEDIUMVIOLETRED\")\n",
    "df['Age'].plot.box(title=\"Age\",ax=axis[1][2],color=\"INDIGO\")\n",
    "df['Outcome'].plot.box(title=\"Outcome\",ax=axis[1][3],color=\"MEDIUMVIOLETRED\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "51222911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI',\n",
       "       'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b68b3662",
   "metadata": {},
   "source": [
    "Outliers treatement in Glucose attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f014c32f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = df.Glucose.quantile(0.25)\n",
    "Q3 = df.Glucose.quantile(0.75)\n",
    "IQR = Q3-Q1\n",
    "lower_limit = Q1-1.5*IQR\n",
    "upper_limit = Q3+1.5*IQR\n",
    "df.loc[df['Glucose']<=lower_limit,'Glucose'] = np.mean(df['Glucose'])\n",
    "df.loc[df['Glucose']>upper_limit, 'Glucose'] = np.mean(df['Glucose'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c92777",
   "metadata": {},
   "source": [
    "Outliers treatement in BloodPressure attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f532db37",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = df.BloodPressure.quantile(0.25)\n",
    "Q3 = df.BloodPressure.quantile(0.75)\n",
    "IQR = Q3-Q1\n",
    "lower_limit = Q1-1.5*IQR\n",
    "upper_limit = Q3+1.5*IQR\n",
    "df.loc[df['BloodPressure']<=lower_limit,'BloodPressure'] = np.mean(df['BloodPressure'])\n",
    "df.loc[df['BloodPressure']>upper_limit,'BloodPressure'] = np.mean(df['BloodPressure'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52b5c51d",
   "metadata": {},
   "source": [
    "Outliers treatement in SkinThickness attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "15f780a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = df.SkinThickness.quantile(0.25)\n",
    "Q3 = df.SkinThickness.quantile(0.75)\n",
    "IQR = Q3-Q1\n",
    "lower_limit = Q1-1.5*IQR\n",
    "upper_limit = Q3+1.5*IQR\n",
    "df.loc[df['SkinThickness']<=lower_limit,'SkinThickness'] = np.mean(df['SkinThickness'])\n",
    "df.loc[df['SkinThickness']>upper_limit,'SkinThickness'] = np.mean(df['SkinThickness'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d4d1472",
   "metadata": {},
   "source": [
    "Outliers treatement in Insulin attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "039b675c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = df.Insulin.quantile(0.25)\n",
    "Q3 = df.Insulin.quantile(0.75)\n",
    "IQR = Q3-Q1\n",
    "lower_limit = Q1-1.5*IQR\n",
    "upper_limit = Q3+1.5*IQR\n",
    "df.loc[df['Insulin']<=lower_limit,'Insulin'] = np.mean(df['Insulin'])\n",
    "df.loc[df['Insulin']>upper_limit,'Insulin'] = np.mean(df['Insulin'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e4bb19a",
   "metadata": {},
   "source": [
    "Outliers treatement in BMI attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7675e2c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = df.BMI.quantile(0.25)\n",
    "Q3 = df.BMI.quantile(0.75)\n",
    "IQR = Q3-Q1\n",
    "lower_limit = Q1-1.5*IQR\n",
    "upper_limit = Q3+1.5*IQR\n",
    "df.loc[df['BMI']<=lower_limit,'BMI'] = np.mean(df['BMI'])\n",
    "df.loc[df['BMI']>upper_limit,'BMI'] = np.mean(df['BMI'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4127e63",
   "metadata": {},
   "source": [
    "Outliers treatement in DiabetesPedigreeFunction attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eb594d39",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = df.DiabetesPedigreeFunction.quantile(0.25)\n",
    "Q3 = df.DiabetesPedigreeFunction.quantile(0.75)\n",
    "IQR = Q3-Q1\n",
    "lower_limit = Q1-1.5*IQR\n",
    "upper_limit = Q3+1.5*IQR\n",
    "df.loc[df['DiabetesPedigreeFunction']<=lower_limit,'DiabetesPedigreeFunction'] = np.mean(df['DiabetesPedigreeFunction'])\n",
    "df.loc[df['DiabetesPedigreeFunction']>upper_limit,'DiabetesPedigreeFunction'] = np.mean(df['DiabetesPedigreeFunction'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72bb1911",
   "metadata": {},
   "source": [
    "Outliers treatement in Age attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "22425d5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = df.Age.quantile(0.25)\n",
    "Q3 = df.Age.quantile(0.75)\n",
    "IQR = Q3-Q1\n",
    "lower_limit = Q1-1.5*IQR\n",
    "upper_limit = Q3+1.5*IQR\n",
    "df.loc[df['Age']<=lower_limit,'Age'] = np.mean(df['Age'])\n",
    "df.loc[df['Age']>upper_limit,'Age'] = np.mean(df['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abc76222",
   "metadata": {},
   "source": [
    "Outliers treatement in Outcome attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c673b02b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = df.Outcome.quantile(0.25)\n",
    "Q3 = df.Outcome.quantile(0.75)\n",
    "IQR = Q3-Q1\n",
    "lower_limit = Q1-1.5*IQR\n",
    "upper_limit = Q3+1.5*IQR\n",
    "df.loc[df['Outcome']<=lower_limit,'Outcome'] = np.mean(df['Outcome'])\n",
    "df.loc[df['Outcome']>upper_limit,'Outcome'] = np.mean(df['Outcome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e6ff2ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Outcome'}>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x540 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [13.50, 7.50]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "figure, axis = plt.subplots(2, 4)\n",
    "  \n",
    "df['Glucose'].plot.box(title=\"Glucose\",ax=axis[0][0],color=\"MEDIUMVIOLETRED\")\n",
    "df['BloodPressure'].plot.box(title=\"Blood Pressure\",ax=axis[0][1],color=\"INDIGO\")\n",
    "df['SkinThickness'].plot.box(title=\"Skin Thickness\",ax=axis[0][2],color=\"MEDIUMVIOLETRED\")\n",
    "df['Insulin'].plot.box(title=\"Insulin\",ax=axis[0][3],color=\"INDIGO\")\n",
    "df['BMI'].plot.box(title=\"BMI\",ax=axis[1][0],color=\"INDIGO\")\n",
    "df['DiabetesPedigreeFunction'].plot.box(title=\"Diabetes Pedigree Function\",ax=axis[1][1],color=\"MEDIUMVIOLETRED\")\n",
    "df['Age'].plot.box(title=\"Age\",ax=axis[1][2],color=\"INDIGO\")\n",
    "df['Outcome'].plot.box(title=\"Outcome\",ax=axis[1][3],color=\"MEDIUMVIOLETRED\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb4df2c",
   "metadata": {},
   "source": [
    "## Exporting Preprocessed Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "90a33093",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('Diabetes-dataset_FS.csv', encoding='utf-8', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}