diabetes_KNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diabetes predict using KNN Classification\n",
    "Ahmad Sotoude <br>\n",
    "<a href=\"https://github.com/ahmadeshanam\">link to github</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "importing necessary libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "importing data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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": [
       "   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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('diabetes.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data shape and features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rows:  767\n",
      "cols:  9\n",
      "columns:  ['Pregnancies' 'Glucose' 'BloodPressure' 'SkinThickness' 'Insulin' 'BMI'\n",
      " 'DiabetesPedigreeFunction' 'Age' 'Outcome']\n"
     ]
    }
   ],
   "source": [
    "print('rows: ',df.shape[0])\n",
    "print('cols: ',df.shape[1])\n",
    "print('columns: ',df.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "checking for null values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 767 entries, 0 to 766\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               767 non-null    int64  \n",
      " 1   Glucose                   767 non-null    int64  \n",
      " 2   BloodPressure             767 non-null    int64  \n",
      " 3   SkinThickness             767 non-null    int64  \n",
      " 4   Insulin                   767 non-null    int64  \n",
      " 5   BMI                       767 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  767 non-null    float64\n",
      " 7   Age                       767 non-null    int64  \n",
      " 8   Outcome                   767 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n",
      "\n",
      "\n",
      "Null Values:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "print('\\n\\nNull Values:')\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "understanding data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>count</th>\n",
       "      <td>767.000000</td>\n",
       "      <td>767.000000</td>\n",
       "      <td>767.000000</td>\n",
       "      <td>767.000000</td>\n",
       "      <td>767.000000</td>\n",
       "      <td>767.000000</td>\n",
       "      <td>767.000000</td>\n",
       "      <td>767.000000</td>\n",
       "      <td>767.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.848761</td>\n",
       "      <td>120.930900</td>\n",
       "      <td>69.104302</td>\n",
       "      <td>20.522816</td>\n",
       "      <td>79.903520</td>\n",
       "      <td>31.994654</td>\n",
       "      <td>0.472081</td>\n",
       "      <td>33.254237</td>\n",
       "      <td>0.349413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.370207</td>\n",
       "      <td>31.977581</td>\n",
       "      <td>19.368410</td>\n",
       "      <td>15.958143</td>\n",
       "      <td>115.283105</td>\n",
       "      <td>7.889095</td>\n",
       "      <td>0.331496</td>\n",
       "      <td>11.762079</td>\n",
       "      <td>0.477096</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>0.243500</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>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.374000</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>140.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.500000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626500</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   767.000000  767.000000     767.000000     767.000000  767.000000   \n",
       "mean      3.848761  120.930900      69.104302      20.522816   79.903520   \n",
       "std       3.370207   31.977581      19.368410      15.958143  115.283105   \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   32.000000   \n",
       "75%       6.000000  140.500000      80.000000      32.000000  127.500000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  767.000000                767.000000  767.000000  767.000000  \n",
       "mean    31.994654                  0.472081   33.254237    0.349413  \n",
       "std      7.889095                  0.331496   11.762079    0.477096  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243500   24.000000    0.000000  \n",
       "50%     32.000000                  0.374000   29.000000    0.000000  \n",
       "75%     36.600000                  0.626500   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "as shown above, values have the minimum of 0 which is impossible<br>\n",
    "for some of them like BMI. We consider them as NULL values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 767 entries, 0 to 766\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               767 non-null    int64  \n",
      " 1   Glucose                   762 non-null    float64\n",
      " 2   BloodPressure             732 non-null    float64\n",
      " 3   SkinThickness             540 non-null    float64\n",
      " 4   Insulin                   394 non-null    float64\n",
      " 5   BMI                       756 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  767 non-null    float64\n",
      " 7   Age                       767 non-null    int64  \n",
      " 8   Outcome                   767 non-null    int64  \n",
      "dtypes: float64(6), int64(3)\n",
      "memory usage: 54.1 KB\n",
      "\n",
      "\n",
      "Null Values:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pregnancies                   0\n",
       "Glucose                       5\n",
       "BloodPressure                35\n",
       "SkinThickness               227\n",
       "Insulin                     373\n",
       "BMI                          11\n",
       "DiabetesPedigreeFunction      0\n",
       "Age                           0\n",
       "Outcome                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['BMI'] == 0) , 'BMI'] = np.nan\n",
    "df.loc[(df['Glucose'] == 0) , 'Glucose'] = np.nan\n",
    "df.loc[(df['BloodPressure'] == 0) , 'BloodPressure'] = np.nan\n",
    "df.loc[(df['SkinThickness'] == 0) , 'SkinThickness'] = np.nan\n",
    "df.loc[(df['Insulin'] == 0) , 'Insulin'] = np.nan\n",
    "\n",
    "df.info()\n",
    "print('\\n\\nNull Values:')\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "since we dont want to lose data, <br>\n",
    "we calculate mean for each age and <br>\n",
    "replace the null values with them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AgeGroup'] = (df['Age'] // 10) * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean BMI in AgeGroup:\n",
      "    AgeGroup        BMI\n",
      "0        20  32.041860\n",
      "1        30  32.670122\n",
      "2        40  34.617797\n",
      "\n",
      "mean SkinThickness in AgeGroup:\n",
      "    AgeGroup  SkinThickness\n",
      "0        20      27.759615\n",
      "1        30      30.620370\n",
      "2        40      31.706667\n",
      "\n",
      "mean Glucose in AgeGroup:\n",
      "    AgeGroup     Glucose\n",
      "0        20  114.668367\n",
      "1        30  126.073171\n",
      "2        40  125.709402\n",
      "\n",
      "mean BloodPressure in AgeGroup:\n",
      "    AgeGroup  BloodPressure\n",
      "0        20      68.821333\n",
      "1        30      73.685897\n",
      "2        40      77.212389\n",
      "\n",
      "mean Insulin in AgeGroup:\n",
      "    AgeGroup     Insulin\n",
      "0        20  139.250000\n",
      "1        30  164.602564\n",
      "2        40  163.913043\n"
     ]
    }
   ],
   "source": [
    "mean_bmi_by_age = df.groupby('AgeGroup')['BMI'].mean().reset_index()\n",
    "print('mean BMI in AgeGroup:\\n',mean_bmi_by_age.head(3))\n",
    "\n",
    "mean_skin_by_age = df.groupby('AgeGroup')['SkinThickness'].mean().reset_index()\n",
    "print('\\nmean SkinThickness in AgeGroup:\\n',mean_skin_by_age.head(3))\n",
    "\n",
    "mean_Glucose_by_age = df.groupby('AgeGroup')['Glucose'].mean().reset_index()\n",
    "print('\\nmean Glucose in AgeGroup:\\n',mean_Glucose_by_age.head(3))\n",
    "\n",
    "mean_BP_by_age = df.groupby('AgeGroup')['BloodPressure'].mean().reset_index()\n",
    "print('\\nmean BloodPressure in AgeGroup:\\n',mean_BP_by_age.head(3))\n",
    "\n",
    "mean_Insulin_by_age = df.groupby('AgeGroup')['Insulin'].mean().reset_index()\n",
    "print('\\nmean Insulin in AgeGroup:\\n',mean_Insulin_by_age.head(3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   AgeGroup  SkinThickness\n",
      "0        20      27.759615\n",
      "1        30      30.620370\n",
      "2        40      31.706667\n",
      "3        50      30.125000\n",
      "4        60      34.545455\n",
      "5        70      18.000000\n",
      "6        80      33.000000 \n",
      "\n",
      "   AgeGroup     Insulin\n",
      "0        20  139.250000\n",
      "1        30  164.602564\n",
      "2        40  163.913043\n",
      "3        50  249.440000\n",
      "4        60  297.750000\n",
      "5        70         NaN\n",
      "6        80   60.000000\n"
     ]
    }
   ],
   "source": [
    "print(mean_skin_by_age,'\\n')\n",
    "print(mean_Insulin_by_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "still there are some inaccuracies in the tables above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_Insulin_by_age.loc[mean_Insulin_by_age['AgeGroup']>60,'Insulin'] = mean_Insulin_by_age['Insulin'].mean()\n",
    "mean_skin_by_age.loc[mean_skin_by_age['AgeGroup']>60,'SkinThickness'] = mean_skin_by_age['SkinThickness'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we replace NaNs with proper values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Null Values:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "AgeGroup                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BMI'] = df.apply(lambda row: mean_bmi_by_age.loc[mean_bmi_by_age['AgeGroup'] == row['AgeGroup'], 'BMI'].values[0] if pd.isna(row['BMI']) else row['BMI'], axis=1)\n",
    "\n",
    "df['SkinThickness'] = df.apply(lambda row: mean_skin_by_age.loc[mean_skin_by_age['AgeGroup'] == row['AgeGroup'], 'SkinThickness'].values[0] if pd.isna(row['SkinThickness']) else row['SkinThickness'], axis=1)\n",
    "\n",
    "df['Insulin'] = df.apply(lambda row: mean_Insulin_by_age.loc[mean_Insulin_by_age['AgeGroup'] == row['AgeGroup'], 'Insulin'].values[0] if pd.isna(row['Insulin']) else row['Insulin'], axis=1)\n",
    "\n",
    "df['Glucose'] = df.apply(lambda row: mean_Glucose_by_age.loc[mean_Glucose_by_age['AgeGroup'] == row['AgeGroup'], 'Glucose'].values[0] if pd.isna(row['Glucose']) else row['Glucose'], axis=1)\n",
    "\n",
    "df['BloodPressure'] = df.apply(lambda row: mean_BP_by_age.loc[mean_BP_by_age['AgeGroup'] == row['AgeGroup'], 'BloodPressure'].values[0] if pd.isna(row['BloodPressure']) else row['BloodPressure'], axis=1)\n",
    "\n",
    "print('\\n\\nNull Values:')\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is cleaned, now we try to understand it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ys situation (Diabetes: T/F)\n",
      " Outcome\n",
      "0    499\n",
      "1    268\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Ys situation (Diabetes: T/F)\\n',df['Outcome'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WLUpJSZEkpaSk6MSJE9q+fbtnzIYNG1RRUaHk5ORqvzYAALBHtfbsLFiwQKNGjdLIkSPPeZgqJiZGixYtOu96SkpKtG/fPs/jgoIC5eXlKTo6WomJiXrooYf05JNP6uqrr1aLFi30+OOPKz4+XgMHDpQktW3bVn379tXYsWO1cOFCnT59WhMmTNDQoUP5JhYAAJBUzbKzbt06JSYmKijIe8eQMUaHDh1SYmKiQkNDNWLEiPOuZ9u2berZs6fn8U/n0YwYMUJZWVl69NFHVVpaqnvvvVcnTpxQt27dtHbtWq+vui9btkwTJkxQr169FBQUpMGDB+u5556rztsCAAAWqlbZadmypQoLC8/6JtTx48fVokULlZdf2LdzevToIWPMOZe7XC7NmDFDM2bMOOeY6OhoLV++/MKCAwCAS061ztk5V0EpKSmp9gUGAQAAasNF7dn56TCTy+XS1KlTVb9+fc+y8vJybdmyxfNNKQAAAH9wUWVn584fv6ZsjNHHH3+s0NBQz7LQ0FAlJSXpkUce8W1CAACAGriosvPuu+9Kku655x49++yzioiIqJVQAAAAvlKtE5SXLFni6xwAAAC14oLLzqBBg5SVlaWIiAgNGjTovGOzs7NrHAzeYi+PVXlFuYKDgp2OAgBAQLngshMZGSmXy+X5M+pWVFiUz+5nlXZ1mmbeOtNHyQAA8G8XXHZ+fuiKw1jO8cX9rNo0auOjNAAA+L9qXWfnu+++06lTpzyPDxw4oGeeeUZvv/22z4IBAAD4QrXKzu23366lS5dKkk6cOKEuXbpozpw5uv3227VgwQKfBgQAAKiJapWdHTt26Oabb5Yk/fOf/1RsbKwOHDigpUuXcl8qAADgV6pVdk6dOqXw8HBJ0ttvv61BgwYpKChIN954ow4cOODTgAAAADVRrbLTqlUrrVq1SocOHdJbb72l3r17S5KOHTvGhQYBAIBfqVbZmTp1qh555BE1b95cycnJSklJkfTjXp6OHTv6NCAAAEBNVOsKynfccYe6deumwsJCJSUleeb36tVLv/nNb3wWDgAAoKaqVXYkKTY2VrGxsV7zunTpUuNAAAAAvlStslNaWqpZs2Zp/fr1OnbsmCoqKryW//e///VJOAAAgJqqVtkZM2aMcnJy9Lvf/U5xcXGe20gAAAD4m2qVnTfffFNr1qxR165dfZ0HAADAp6r1bawrrrhC0dHRvs4CAADgc9UqO3/+8581depUr/tjAQAA+KNqHcaaM2eO9u/fryZNmqh58+a67LLLvJbv2LHDJ+EAAABqqlplZ+DAgT6OAQAAUDuqVXamTZvm6xwAAAC1olrn7EjSiRMn9Pe//10ZGRk6fvy4pB8PXx0+fNhn4QAAAGqqWnt28vPzlZqaqsjISH3xxRcaO3asoqOjlZ2drYMHD2rp0qW+zgkAAFAt1dqzk56erpEjR2rv3r0KCwvzzO/Xr582bdrks3AAAAA1Va2ys3XrVo0bN+6s+VdeeaWKiopqHAoAAMBXqlV23G63iouLz5r/+eefq3HjxjUOBQAA4CvVKju33XabZsyYodOnT0uSXC6XDh48qEmTJmnw4ME+DQgAAFAT1So7c+bMUUlJiRo3bqzvvvtO3bt3V6tWrRQeHq6ZM2f6OiMAAEC1VevbWJGRkVq3bp3ef/997dq1SyUlJerUqZNSU1N9nQ8AAKBGLrrsVFRUKCsrS9nZ2friiy/kcrnUokULxcbGyhgjl8tVGzkBAACq5aIOYxljdNttt2nMmDE6fPiw2rdvr2uvvVYHDhzQyJEj9Zvf/Ka2cgIAAFTLRe3ZycrK0qZNm7R+/Xr17NnTa9mGDRs0cOBALV26VL///e99GhIAAKC6LmrPzooVK/TYY4+dVXQk6dZbb9XkyZO1bNkyn4UDAACoqYsqO/n5+erbt+85l6elpWnXrl01DgUAAOArF1V2jh8/riZNmpxzeZMmTfTNN9/UOBQAAICvXFTZKS8vV0jIuU/zCQ4O1pkzZ2ocCgAAwFcu6gRlY4xGjhwpt9td6fKysjKfhAIAAPCViyo7I0aMqHIM38QCAAD+5KLKzpIlS2orxzk1b95cBw4cOGv+//7v/2r+/Pnq0aOHcnJyvJaNGzdOCxcurKuIAADAj1XrdhF1aevWrSovL/c8/uSTT/TrX/9ad955p2fe2LFjNWPGDM/j+vXr12lGAADgv/y+7DRu3Njr8axZs9SyZUt1797dM69+/fqKjY294HWWlZV5nV9UXFxc86AAAMAvVeuu50754Ycf9PLLL2vUqFFe9+BatmyZGjVqpHbt2ikjI0OnTp0673oyMzMVGRnpmRISEmo7OgAAcIjf79n5uVWrVunEiRMaOXKkZ95vf/tbNWvWTPHx8crPz9ekSZO0Z88eZWdnn3M9GRkZSk9P9zwuLi6m8AAAYKmAKjuLFi1SWlqa4uPjPfPuvfdez5/bt2+vuLg49erVS/v371fLli0rXY/b7T7n1+cBAIBdAuYw1oEDB/TOO+9ozJgx5x2XnJwsSdq3b19dxAIAAH4uYMrOkiVLFBMTo/79+593XF5eniQpLi6uDlIBAAB/FxCHsSoqKrRkyRKNGDHC63YV+/fv1/Lly9WvXz81bNhQ+fn5mjhxom655RZ16NDBwcQAAMBfBETZeeedd3Tw4EGNGjXKa35oaKjeeecdPfPMMyotLVVCQoIGDx6sKVOmOJQUAAD4m4AoO71795Yx5qz5CQkJZ109GQAA4OcC5pwdAACA6qDsAAAAq1F2AACA1Sg7AADAapQdAABgNcoOAACwGmUHAABYjbIDAACsRtkBAABWo+wAAACrUXYAAIDVKDsAAMBqlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNUoOwAAwGqUHQAAYDXKDgAAsBplBwAAWI2yAwAArEbZAQAAVqPsAAAAq1F2AACA1Sg7AADAapQdAABgNcoO/EZ5RblfrgsAENhCnA4A/CQ4KFjDs4dr91e7a7Seto3batmgZT5KBQAIdJQd+JXdX+3WzqKdTscAAFiEw1gAAMBqlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEClK+uJcQ1iQDYjq+eAwHKF9cl4ppEAC4FlB0ggHFdIgComl8fxnriiSfkcrm8pjZt2niWf//99xo/frwaNmyoyy+/XIMHD9bRo0cdTAwAAPyNX5cdSbr22mtVWFjomTZv3uxZNnHiRP373//Wa6+9ppycHB05ckSDBg1yMC1sxD27ACCw+f1hrJCQEMXGxp41/+TJk1q0aJGWL1+uW2+9VZK0ZMkStW3bVh9++KFuvPHGc66zrKxMZWVlnsfFxcW+Dw5rcM8uAAhsfl929u7dq/j4eIWFhSklJUWZmZlKTEzU9u3bdfr0aaWmpnrGtmnTRomJicrNzT1v2cnMzNT06dPrIr71Yi+PVXlFuYKDgp2OUqs4NwYAApdfl53k5GRlZWWpdevWKiws1PTp03XzzTfrk08+UVFRkUJDQxUVFeX1nCZNmqioqOi8683IyFB6errncXFxsRISEmrjLVgvKizKJ3s+0q5O08xbZ/owGQAAP/LrspOWlub5c4cOHZScnKxmzZrp1VdfVb169aq9XrfbLbfb7YuI+P9quuejTaM2VQ8CAKAa/P4E5Z+LiorSNddco3379ik2NlY//PCDTpw44TXm6NGjlZ7jAwAALk0BVXZKSkq0f/9+xcXF6frrr9dll12m9evXe5bv2bNHBw8eVEpKioMpAQCAP/Hrw1iPPPKIBgwYoGbNmunIkSOaNm2agoODNWzYMEVGRmr06NFKT09XdHS0IiIidP/99yslJeW8JycDAIBLi1+XnS+//FLDhg3T119/rcaNG6tbt2768MMP1bhxY0nSX//6VwUFBWnw4MEqKytTnz599MILLzicGgAA+BO/LjsrV6487/KwsDDNnz9f8+fPr6NEAAAg0ATUOTsAAAAXi7IDAACsRtkBAABWo+wAAACrUXYAAIDVKDsAAMBqlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNUoOwAAwGqUHQAAYDXKDgAAsBplBwAAWI2yAwAArEbZAQAAVqPsAAAAq1F2AACA1Sg7AADAapQdAABgNcoOAACwGmUHAABYjbIDAACsRtkBAABWo+wAAACrUXYAAIDVKDsAAMBqlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNUoOwAAwGqUHQAAYDXKDgAAsJpfl53MzEx17txZ4eHhiomJ0cCBA7Vnzx6vMT169JDL5fKa/vCHPziUGAAA+Bu/Ljs5OTkaP368PvzwQ61bt06nT59W7969VVpa6jVu7NixKiws9ExPPfWUQ4kBAIC/CXE6wPmsXbvW63FWVpZiYmK0fft23XLLLZ759evXV2xsbF3HAwAAAcCv9+z80smTJyVJ0dHRXvOXLVumRo0aqV27dsrIyNCpU6fOu56ysjIVFxd7TQAAwE5+vWfn5yoqKvTQQw+pa9euateunWf+b3/7WzVr1kzx8fHKz8/XpEmTtGfPHmVnZ59zXZmZmZo+fXpdxAY8Yi+PVXlFuYKDgp2OAgCXlIApO+PHj9cnn3yizZs3e82/9957PX9u37694uLi1KtXL+3fv18tW7asdF0ZGRlKT0/3PC4uLlZCQkLtBAf+v6iwKAUHBWt49nDt/mp3jdaVdnWaZt4600fJAMBuAVF2JkyYoNWrV2vTpk1q2rTpeccmJydLkvbt23fOsuN2u+V2u32eE/7B3/eg7P5qt3YW7azROto0auOjNL7ly+3uzz9DAIHFr8uOMUb333+/Xn/9dW3cuFEtWrSo8jl5eXmSpLi4uFpOB3/FHhTn+Gq7t23cVssGLfNRKgCXOr8uO+PHj9fy5cv1r3/9S+Hh4SoqKpIkRUZGql69etq/f7+WL1+ufv36qWHDhsrPz9fEiRN1yy23qEOHDg6nh9Ns3oPiK7WxF8wX2x0AfMmvy86CBQsk/XjhwJ9bsmSJRo4cqdDQUL3zzjt65plnVFpaqoSEBA0ePFhTpkxxIC0QeNgLBuBS4Ndlxxhz3uUJCQnKycmpozSAvdgLBsBmAXWdHQAAgItF2QFgvfKKcr9aD4C65deHsQDAF3xxXhLfEAMCF2UHwCWBb4kBly4OYwEAAKtRdgAAgNUoOwAAwGqUHQAAYDXKDgAAsBplBwAAWI2yAwAArEbZAQAAVqPsAAAAq1F2AMABvrzPFvfsAs6P20UAgAN8cb8uiXt2AReCsgMADuF+XUDd4DAWAACwGmUHgN+JvTyW81AA+AyHsQD4naiwKJ+d05J2dZpm3jrTR8kABCLKDgC/5YtzWto0auOjNAACFYexAACA1Sg7AADAapQdAABgNcoOAFyAS+UbYlzZGTbiBGUAuACXyjfEuLIzbETZAYCLcCl8Q4wrO8M2HMYCAABWo+wAQAC7FM4l4jwi1BSHsQAggF0K5xJxHhFqirIDABaw/VwiziNCTXAYCwAAWI2yAwAArEbZAQAAVqPsAAAAq1F2AACA1Sg7AADAapQdAIDPXQoXO0Tg4Do7AACf89XFDv31QocILJQdAECtqenFAH15ocOf9jYFBwX7ZH2+XBdqF2UHAHBJ8OWtNbj1RGCxpuzMnz9fTz/9tIqKipSUlKR58+apS5cuTscCAPgZbj1x6bHiBOVXXnlF6enpmjZtmnbs2KGkpCT16dNHx44dczoaAABwmBVlZ+7cuRo7dqzuuece/epXv9LChQtVv359LV682OloAAAL+frbZv74zTWb3l/AH8b64YcftH37dmVkZHjmBQUFKTU1Vbm5uZU+p6ysTGVlZZ7HJ0+elCQVFxfXSsZWDVqpPLJmP+j40HgVFxdbuy5/zHQprMsfM/nruvwx06WwLn/MJEm/iviVSktKNfv92Tp08lCN1pUQmaBJXSfVaB21xd/f30//bhtjzj/QBLjDhw8bSeaDDz7wmv/HP/7RdOnSpdLnTJs2zUhiYmJiYmJismA6dOjQebtCwO/ZqY6MjAylp6d7HldUVOj48eNq2LChXC6Xz16nuLhYCQkJOnTokCIiIny23pryx1z+mEki18Xwx0ySf+byx0ySf+byx0wSuS5GbWYyxujbb79VfHz8eccFfNlp1KiRgoODdfToUa/5R48eVWxsbKXPcbvdcrvdXvOioqJqK6IiIiL85kP3c/6Yyx8zSeS6GP6YSfLPXP6YSfLPXP6YSSLXxaitTJGRkVWOCfgTlENDQ3X99ddr/fr1nnkVFRVav369UlJSHEwGAAD8QcDv2ZGk9PR0jRgxQjfccIO6dOmiZ555RqWlpbrnnnucjgYAABxmRdkZMmSIvvrqK02dOlVFRUW67rrrtHbtWjVp0sTRXG63W9OmTTvrkJnT/DGXP2aSyHUx/DGT5J+5/DGT5J+5/DGTRK6L4Q+ZXMZU9X0tAACAwBXw5+wAAACcD2UHAABYjbIDAACsRtkBAABWo+zUUGZmpjp37qzw8HDFxMRo4MCB2rNnj9eYl156ST169FBERIRcLpdOnDjhF7kkKTc3V7feeqsaNGigiIgI3XLLLfruu+9qLdeCBQvUoUMHz8WlUlJS9Oabb3qWO7Gtqsok1f12+qVZs2bJ5XLpoYce8sxzYltdSC6p7rfXE088IZfL5TW1adPGs9yJbVVVJsm5z9Xhw4d19913q2HDhqpXr57at2+vbdu2eZZnZ2erd+/enqvK5+Xl1XqmC8klSbt379Ztt92myMhINWjQQJ07d9bBgwdrJU/z5s3P+hm6XC6NHz9eknO/g1Xlkur+s1VeXq7HH39cLVq0UL169dSyZUv9+c9/9rpnlVOfK4myU2M5OTkaP368PvzwQ61bt06nT59W7969VVpa6hlz6tQp9e3bV4899phf5crNzVXfvn3Vu3dvffTRR9q6dasmTJigoKDa+1g0bdpUs2bN0vbt27Vt2zbdeuutuv322/Xpp59KcmZbVZXJie30c1u3btWLL76oDh06eM13YltdSC6ntte1116rwsJCz7R582bPMqe21fkyObWdvvnmG3Xt2lWXXXaZ3nzzTX322WeaM2eOrrjiCs+Y0tJSdevWTbNnz67VLBeba//+/erWrZvatGmjjRs3Kj8/X48//rjCwsJqJdPWrVu9fn7r1q2TJN15552SnPtcVZXLic/W7NmztWDBAj3//PPavXu3Zs+eraeeekrz5s3zjHHic+Xhk7txwuPYsWNGksnJyTlr2bvvvmskmW+++cYvciUnJ5spU6bUeZZfuuKKK8zf//53r3lObqtfZnJyO3377bfm6quvNuvWrTPdu3c3Dz744FljnNhW58vlxPaaNm2aSUpKqnJcXW6rqjI59bmaNGmS6dat2wWNLSgoMJLMzp07azeUubBcQ4YMMXfffXetZzmXBx980LRs2dJUVFR4zXf676tf5nLis9W/f38zatQor3mDBg0yw4cPP2tsXX6ufsKeHR87efKkJCk6OtrhJN5+mevYsWPasmWLYmJidNNNN6lJkybq3r271/88a1t5eblWrlyp0tJSv7m1xy8zOb2dxo8fr/79+ys1NbVOXu9CnSuXk9tr7969io+P11VXXaXhw4fX2qENX2Rycju98cYbuuGGG3TnnXcqJiZGHTt21N/+9rdaf92a5qqoqNCaNWt0zTXXqE+fPoqJiVFycrJWrVpVJ/l++OEHvfzyyxo1apRPbxhdU7/M5dRn66abbtL69ev1+eefS5J27dqlzZs3Ky0trVZf94LVWa26BJSXl5v+/fubrl27VrrcqfZfWa7c3FwjyURHR5vFixebHTt2mIceesiEhoaazz//vFbz5OfnmwYNGpjg4GATGRlp1qxZc9aYut5W58rk5HZasWKFadeunfnuu++MMcZv9uycL5dT2+s///mPefXVV82uXbvM2rVrTUpKiklMTDTFxcVe4+pyW50vk5OfK7fbbdxut8nIyDA7duwwL774ogkLCzNZWVlnja3L/4FXlauwsNBIMvXr1zdz5841O3fuNJmZmcblcpmNGzfWer5XXnnFBAcHm8OHD5+1zMk9O7/M5dRnq7y83EyaNMm4XC4TEhJiXC6X+b//+79KxzqxZ4ey40N/+MMfTLNmzcyhQ4cqXe7UL0Rlud5//30jyWRkZHiNbd++vZk8eXKt5ikrKzN79+4127ZtM5MnTzaNGjUyn376qdeYut5W58rk1HY6ePCgiYmJMbt27fLM84eyU1UuJz9XP/fNN9+YiIgIvzo8+vNMTm6nyy67zKSkpHjNu//++82NN9541ti6/EepqlyHDx82ksywYcO8xgwYMMAMHTq01vP17t3b/M///E+ly5z8XP0yl1OfrRUrVpimTZuaFStWmPz8fLN06VITHR3teIn+CYexfGTChAlavXq13n33XTVt2tTpOB7nyhUXFydJ+tWvfuU1vm3btrW++z80NFStWrXS9ddfr8zMTCUlJenZZ5+t1desbianttP27dt17NgxderUSSEhIQoJCVFOTo6ee+45hYSEqLy8vNZeuya5frofnROfq5+LiorSNddco3379tXZa1bl55mc/P2Li4tz/OdTmapyNWrUSCEhIY5kP3DggN555x2NGTOmVl/nYlWWy6nP1h//+EdNnjxZQ4cOVfv27fW73/1OEydOVGZmZq295sWg7NSQMUYTJkzQ66+/rg0bNqhFixZOR5JUda7mzZsrPj7+rK+jf/7552rWrFldRlVFRYXKysrq9DWr8lMmp7ZTr1699PHHHysvL88z3XDDDRo+fLjy8vIUHBxca69dk1xXXXWVX3yuSkpKtH//fs9f/P7g55mc/P3r2rWr4z+fylSVKzQ0VJ07d3Yk+5IlSxQTE6P+/fvX6utcrMpyOfXZOnXq1Fnf9goODlZFRUWtveZFqbN9SJa67777TGRkpNm4caMpLCz0TKdOnfKMKSwsNDt37jR/+9vfjCSzadMms3PnTvP11187muuvf/2riYiIMK+99prZu3evmTJligkLCzP79u2rtVyTJ082OTk5pqCgwOTn55vJkycbl8tl3n77bWOMM9uqqkxObKfK/PIwlhPb6kJyObG9Hn74YbNx40ZTUFBg3n//fZOammoaNWpkjh07ZoxxZltVlcmpz9VHH31kQkJCzMyZM83evXvNsmXLTP369c3LL7/sGfP111+bnTt3mjVr1hhJZuXKlWbnzp2msLDQ0VzZ2dnmsssuMy+99JLZu3evmTdvngkODjbvvfdereUqLy83iYmJZtKkSWctc/J38Hy5nPhsjRgxwlx55ZVm9erVpqCgwGRnZ5tGjRqZRx991DPGic/VTyg7NSSp0mnJkiWeMdOmTatyjBO5jDEmMzPTNG3a1NSvX9+kpKTU6l8axhgzatQo06xZMxMaGmoaN25sevXq5SkVxjizrarKZEzdb6fK/LJUOLGtLiSXMXW/vYYMGWLi4uJMaGioufLKK82QIUO8/mJ3YltVlckY5z5X//73v027du2M2+02bdq0MS+99JLX8iVLllS6vaZNm+ZoLmOMWbRokWnVqpUJCwszSUlJZtWqVbWa6a233jKSzJ49e85a5uTv4PlyGVP3n63i4mLz4IMPmsTERBMWFmauuuoq86c//cmUlZV5xjj1uTLGGJcxP7u8IQAAgGU4ZwcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNUoOwAAwGqUHQAAYDXKDgAAsBplBwAAWI2yAyAg5ebmKjg42O9uzgjA/3C7CAABacyYMbr88su1aNEi7dmzR/Hx8U5HAuCn2LMDIOCUlJTolVde0X333af+/fsrKyvLa/kbb7yhq6++WmFhYerZs6f+8Y9/yOVy6cSJE54xmzdv1s0336x69eopISFBDzzwgEpLS+v2jQCoE5QdAAHn1VdfVZs2bdS6dWvdfffdWrx4sX7aSV1QUKA77rhDAwcO1K5duzRu3Dj96U9/8nr+/v371bdvXw0ePFj5+fl65ZVXtHnzZk2YMMGJtwOglnEYC0DA6dq1q+666y49+OCDOnPmjOLi4vTaa6+pR48emjx5stasWaOPP/7YM37KlCmaOXOmvvnmG0VFRWnMmDEKDg7Wiy++6BmzefNmde/eXaWlpQoLC3PibQGoJezZARBQ9uzZo48++kjDhg2TJIWEhGjIkCFatGiRZ3nnzp29ntOlSxevx7t27VJWVpYuv/xyz9SnTx9VVFSooKCgbt4IgDoT4nQAALgYixYt0pkzZ7xOSDbGyO126/nnn7+gdZSUlGjcuHF64IEHzlqWmJjos6wA/ANlB0DAOHPmjJYuXao5c+aod+/eXssGDhyoFStWqHXr1vrPf/7jtWzr1q1ejzt16qTPPvtMrVq1qvXMAJzHOTsAAsaqVas0ZMgQHTt2TJGRkV7LJk2apA0bNujVV19V69atNXHiRI0ePVp5eXl6+OGH9eWXX+rEiROKjIxUfn6+brzxRo0aNUpjxoxRgwYN9Nlnn2ndunUXvHcIQODgnB0AAWPRokVKTU09q+hI0uDBg7Vt2zZ9++23+uc//6ns7Gx16NBBCxYs8Hwby+12S5I6dOignJwcff7557r55pvVsWNHTZ06lWv1AJZizw4A682cOVMLFy7UoUOHnI4CwAGcswPAOi+88II6d+6shg0b6v3339fTTz/NNXSASxhlB4B19u7dqyeffFLHjx9XYmKiHn74YWVkZDgdC4BDOIwFAACsxgnKAADAapQdAABgNcoOAACwGmUHAABYjbIDAACsRtkBAABWo+wAAACrUXYAAIDV/h8yROpRxm9i7QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins_num = int((86-21)/5)\n",
    "plt.hist(df['Age'],bins=24,ec='white',color='green')\n",
    "plt.xticks(np.arange(21,86,5))\n",
    "plt.ylabel('Density')\n",
    "plt.xlabel('Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['BMI'],ec='white',color='green', bins=24)\n",
    "plt.xticks(np.arange(df['BMI'].min(),df['BMI'].max()+10,10))\n",
    "plt.ylabel('Density')\n",
    "plt.xlabel('BMI')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Glucose'],ec='white',color='green', bins=24)\n",
    "plt.xticks(np.arange(df['Glucose'].min(),df['Glucose'].max()+10,10))\n",
    "plt.ylabel('Density')\n",
    "plt.xlabel('Glucose')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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66KGHqiQjAACwtvMuO388dVVVp7EeffRRdejQQc8884z69eundevWafbs2Zo9e7akU3eFjRgxQk8//bSaNm2q+Ph4jR07VrGxserdu3eVZAQAANZWoWt2fvvtNxljPLee7969W4sWLdJVV12lrl27+izcddddp0WLFikjI0Pjx49XfHy8pk2bptTUVM82jz/+uI4ePaohQ4aosLBQnTp10tKlS336vB8AAFB9Vajs9OrVS3369NGDDz6owsJCtWvXTkFBQfrll180ZcoUn55CuvXWW3XrrbeWu97hcGj8+PEaP368z34mUB2UuksVGBDo7xgAYHkVKjubNm3S1KlTJUkffPCBXC6XcnJy9OGHHyozM5PrZYAq4Itbl1Oapmhi54k+TAUA1lOhsnPs2DGFhYVJkr744gv16dNHAQEBuv7667V7926fBgRQvsreupxweYIP0wCANVXou7GaNGmixYsXKy8vT59//rnnOp0DBw5c9AcNAgAAXIgKlZ3MzEw99thjaty4sdq3b6+kpCRJp47ytGnTxqcBAQAAKqNCp7Fuv/12derUSfv371erVq084126dNHf/vY3n4UDAACorAqVHenU04tPP8H4tHbt2lU6EAAAgC9VqOwcPXpUzz77rLKzs3XgwAG53W6v9Tt27PBJOAAAgMqqUNkZPHiwVq5cqXvvvVcxMTGer5EAAACwmgqVnSVLlujTTz9Vx44dfZ0HAADApyp0N1adOnUUGRnp6ywAAAA+V6GyM2HCBGVmZurYsWO+zgMAAOBTFTqN9fzzz+vnn39WdHS0GjdurJo1a3qt37Rpk0/CAQAAVFaFyk7v3r19HAMAAODiqFDZGTdunK9zAAAAXBQVumZHkgoLC/Xqq68qIyNDhw4dknTq9NXevXt9Fg4AAKCyKnRk57vvvlNycrIiIiK0a9cu3X///YqMjNTChQu1Z88ezZs3z9c5AQAAKqRCR3ZGjhyp/v37a9u2bQoODvaM9+jRQ6tWrfJZOAAAgMqqUNlZv369HnjggTPG69evr/z8/EqHAgAA8JUKlR2n06mioqIzxn/66SdFRUVVOhQAAICvVKjs3HbbbRo/frxOnjwpSXI4HNqzZ49Gjx6tvn37+jQgAABAZVSo7Dz//PMqLi5WVFSUfvvtN910001q0qSJwsLCNHHiRF9nBAAAqLAK3Y0VERGhZcuW6euvv9a3336r4uJiXXPNNUpOTvZ1PgAAgEq54LLjdrs1d+5cLVy4ULt27ZLD4VB8fLxcLpeMMXI4HBcjJwAAQIVc0GksY4xuu+02DR48WHv37lWLFi3UvHlz7d69W/3799ff/va3i5UTAACgQi7oyM7cuXO1atUqZWdn6+abb/Za9+WXX6p3796aN2+e7rvvPp+GBAAAqKgLOrLz9ttv64knnjij6EhS586dNWbMGM2fP99n4QAAACrrgsrOd999p+7du5e7PiUlRd9++22lQwEAAPjKBZWdQ4cOKTo6utz10dHROnz4cKVDAQAA+MoFlZ3S0lLVqFH+ZT6BgYH6/fffKx0KAADAVy7oAmVjjPr37y+n01nm+pKSEp+EAgAA8JULKjtpaWnn3IY7sQAAgJVcUNmZM2fOxcoBAABwUVTou7EAAACqC8oOAACwNcoOAACwNcoOAACwNcoOAACwNcoOAACwNcoOAFRjrloulbpLfTafL+cCrOKCnrMDALCW2sG1FRgQqNSFqco9mFupuRKjEjW/z3wfJQOsg7IDADaQezBXOfk5/o4BWBKnsQAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1Vq7Lz7LPPyuFwaMSIEZ6x48ePKz09XXXr1lWtWrXUt29fFRQU+C8kAACwlGpTdtavX69//vOfatmypdf4o48+qo8//ljvv/++Vq5cqX379qlPnz5+SgkAAKymWpSd4uJipaam6pVXXlGdOnU840eOHNFrr72mKVOmqHPnzrr22ms1Z84cffPNN1qzZk2585WUlKioqMhrAQAA9lQtyk56erpuueUWJScne41v3LhRJ0+e9BpPSEhQXFycVq9eXe58WVlZioiI8CwNGza8aNkBAIB/Wb7svPPOO9q0aZOysrLOWJefn6+goCDVrl3bazw6Olr5+fnlzpmRkaEjR454lry8PF/HBgAAFlHD3wHOJi8vT8OHD9eyZcsUHBzss3mdTqecTqfP5gMAANZl6SM7Gzdu1IEDB3TNNdeoRo0aqlGjhlauXKnp06erRo0aio6O1okTJ1RYWOj1voKCArlcLv+EBgAAlmLpIztdunTR999/7zU2YMAAJSQkaPTo0WrYsKFq1qyp7Oxs9e3bV5K0detW7dmzR0lJSf6IDAAALMbSZScsLExXX32111hoaKjq1q3rGR80aJBGjhypyMhIhYeHa9iwYUpKStL111/vj8gAAMBiLF12zsfUqVMVEBCgvn37qqSkRN26ddPLL7/s71gAAMAiql3ZWbFihdfr4OBgzZgxQzNmzPBPIAAAYGmWvkAZAACgsig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AADA1ig7AABJkquWS6XuUp/N58u5gMqo4e8AAABrqB1cW4EBgUpdmKrcg7mVmisxKlHz+8z3UTKgcig7AAAvuQdzlZOf4+8YgM9wGgsAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQcAANgaZQeoQqXuUn9HAIBLTg1/BwAuJYEBgUpdmKrcg7mVmielaYomdp7oo1QAYG+UHaCK5R7MVU5+TqXmSLg8wUdpAMD+OI0FAABsjbIDnAPX2QBA9cZpLOAcuM4GAKo3yg5wHrjOBgCqL05jAQAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW6PsAAAAW7N02cnKytJ1112nsLAw1atXT71799bWrVu9tjl+/LjS09NVt25d1apVS3379lVBQYGfEgMAAKuxdNlZuXKl0tPTtWbNGi1btkwnT55U165ddfToUc82jz76qD7++GO9//77Wrlypfbt26c+ffr4MTUAALASSz9UcOnSpV6v586dq3r16mnjxo268cYbdeTIEb322mtasGCBOnfuLEmaM2eOEhMTtWbNGl1//fX+iA0AACzE0kd2/uzIkSOSpMjISEnSxo0bdfLkSSUnJ3u2SUhIUFxcnFavXl3uPCUlJSoqKvJaAACAPVWbsuN2uzVixAh17NhRV199tSQpPz9fQUFBql27tte20dHRys/PL3eurKwsRUREeJaGDRtezOgAAMCPqk3ZSU9P15YtW/TOO+9Ueq6MjAwdOXLEs+Tl5fkgIQAAsCJLX7Nz2tChQ/XJJ59o1apVatCggWfc5XLpxIkTKiws9Dq6U1BQIJfLVe58TqdTTqfzYkYGAAAWYekjO8YYDR06VIsWLdKXX36p+Ph4r/XXXnutatasqezsbM/Y1q1btWfPHiUlJVV1XAAAYEGWPrKTnp6uBQsW6F//+pfCwsI81+FEREQoJCREERERGjRokEaOHKnIyEiFh4dr2LBhSkpK4k4sAAAgyeJlZ+bMmZKkv/zlL17jc+bMUf/+/SVJU6dOVUBAgPr27auSkhJ169ZNL7/8chUnBQAAVmXpsmOMOec2wcHBmjFjhmbMmFEFiQAAQHVj6Wt2AAAAKouyAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwAAbI2yAwDwOVctl0rdpT6Zy1fz4NJVw98BAAD2Uzu4tgIDApW6MFW5B3MrPE9iVKLm95nvw2S4FFF2AAAXTe7BXOXk5/g7Bi5xnMYCAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtkBAAC2RtmBLfHFgQCA0/huLNiSL76AUJJSmqZoYueJPkoFAPAHyg5syxdfQJhweYKP0gAA/IXTWAAAy3LVcvn0tDSnuC9NHNkBAFhW7eDaPjstnRiVqPl95vsoGaoTyg4AwPJ8cVoaly5OYwEAUAGcXqs+OLIDAEAFcHqt+qDsAABQQZxeqx44jQUAAGyNsgPL4Jw1AOBi4DQWLIOnHgMALgbKDiyFpx4DAHyN01gAgEuCr5/G7Cs8Jfri48gOAOCS4MunMfvydDlPib74KDsAgEuKVU+Xcxv7xcNprGrEqoc5OWQKALAyjuxUI1Y9zOmLXNxBBQC4WCg71YxVD3NWNhd3UAEALhbbnMaaMWOGGjdurODgYLVv317r1q3zdyRJnOIBAFQdX97ZZafLHWxxZOfdd9/VyJEjNWvWLLVv317Tpk1Tt27dtHXrVtWrV8+v2ax45T8AwJ58dWfX6b9zrHjpREXYouxMmTJF999/vwYMGCBJmjVrlj799FO9/vrrGjNmjJ/TWffKfwCAPfnq0gKrXjpxoap92Tlx4oQ2btyojIwMz1hAQICSk5O1evXqMt9TUlKikpISz+sjR45IkoqKii5KxiahTVQaUblDeLFBsSoqKvLJXE1Cm/j8d61sLl/+fsxVvTNZdS4rZroU5rJipkthLqv/nXPa6XmNMWff0FRze/fuNZLMN9984zU+atQo065duzLfM27cOCOJhYWFhYWFxQZLXl7eWbtCtT+yUxEZGRkaOXKk57Xb7dahQ4dUt25dORwOn/2coqIiNWzYUHl5eQoPD/fZvBcbuasWuasWuasWuavWpZbbGKNff/1VsbGxZ92u2pedyy+/XIGBgSooKPAaLygokMvlKvM9TqdTTqfTa6x27doXK6LCw8Or1YfuNHJXLXJXLXJXLXJXrUspd0RExDm3qfa3ngcFBenaa69Vdna2Z8ztdis7O1tJSUl+TAYAAKyg2h/ZkaSRI0cqLS1Nbdu2Vbt27TRt2jQdPXrUc3cWAAC4dNmi7Nx55506ePCgMjMzlZ+fr9atW2vp0qWKjo72ay6n06lx48adccrM6shdtchdtchdtchdtchdNocx57pfCwAAoPqq9tfsAAAAnA1lBwAA2BplBwAA2BplBwAA2Bplp5KysrJ03XXXKSwsTPXq1VPv3r21devWMrc1xiglJUUOh0OLFy+u2qBlmDlzplq2bOl5iFNSUpKWLFlyxnZWy/1Hzz77rBwOh0aMGHHGOqvl/sc//iGHw+G1JCSc+QWvVsu9d+9e3XPPPapbt65CQkLUokULbdiwocxtH3zwQTkcDk2bNq1qQ5ahcePGZ+xvh8Oh9PR0r+2stL9LS0s1duxYxcfHKyQkRFdeeaUmTJhQ7vf+WGl///rrrxoxYoQaNWqkkJAQdejQQevXry9zW3/mXrVqlXr27KnY2Ngy/zc3xigzM1MxMTEKCQlRcnKytm3bVuZcJSUlat26tRwOhzZv3uzX3AsXLlTXrl093wRwtjxV+Zk/W+6TJ09q9OjRatGihUJDQxUbG6v77rtP+/btK3Ouyuxvyk4lrVy5Uunp6VqzZo2WLVumkydPqmvXrjp69OgZ206bNs2nX0dRWQ0aNNCzzz6rjRs3asOGDercubN69eqlH374wWs7q+U+bf369frnP/+pli1blrneirmbN2+u/fv3e5avvvrqjG2slPvw4cPq2LGjatasqSVLlujHH3/U888/rzp16pyx7aJFi7RmzZpzPra9qqxfv95rXy9btkySdMcdd3htZ6X9PWnSJM2cOVMvvfSScnNzNWnSJE2ePFkvvvjiGdtabX8PHjxYy5Yt05tvvqnvv/9eXbt2VXJysvbu3eu1nb9zHz16VK1atdKMGTPKXD958mRNnz5ds2bN0tq1axUaGqpu3brp+PHjZ2z7+OOPV9nvca7cR48eVadOnTRp0qRzzlWVn/mz5T527Jg2bdqksWPHatOmTVq4cKG2bt2q2267rcy5KrW/K/c1nPizAwcOGElm5cqVXuM5OTmmfv36Zv/+/UaSWbRokX8CnkOdOnXMq6++6nlt1dy//vqradq0qVm2bJm56aabzPDhw73WWzH3uHHjTKtWrc66jdVyjx492nTq1Omc2/3nP/8x9evXN1u2bDGNGjUyU6dOvfjhLtDw4cPNlVdeadxut2fMavv7lltuMQMHDvQa69Onj0lNTfUas9r+PnbsmAkMDDSffPKJ1/g111xj/v73v3teWy33n/83d7vdxuVymf/5n//xjBUWFhqn02nefvttr/d+9tlnJiEhwfzwww9GksnJyami1Gfm/qOdO3eeNY8/P/Pn8/PWrVtnJJndu3d7jVd2f3Nkx8eOHDkiSYqMjPSMHTt2TP/93/+tGTNmlPt9Xf5WWlqqd955R0ePHvV8zYaVc6enp+uWW25RcnLyGeusnHvbtm2KjY3VFVdcodTUVO3Zs8ezzoq5P/roI7Vt21Z33HGH6tWrpzZt2uiVV17x2sbtduvee+/VqFGj1Lx5cz8lPbsTJ07orbfe0sCBAz3/orXi/u7QoYOys7P1008/SZK+/fZbffXVV0pJSfFsY8X9/fvvv6u0tFTBwcFe4yEhIZ6jl1bM/Wc7d+5Ufn6+158rERERat++vVavXu0ZKygo0P33368333xTl112mT+iVogVP/N/duTIETkcDq/vq/TF/rbFE5Stwu12a8SIEerYsaOuvvpqz/ijjz6qDh06qFevXn5MV7bvv/9eSUlJOn78uGrVqqVFixbpqquukmTd3O+88442bdpU7vUAVs3dvn17zZ07V82aNdP+/fv11FNP6YYbbtCWLVsUFhZmydw7duzQzJkzNXLkSD3xxBNav369HnnkEQUFBSktLU3SqVMvNWrU0COPPOLntOVbvHixCgsL1b9/f8+YFff3mDFjVFRUpISEBAUGBqq0tFQTJ05UamqqZxsr7u+wsDAlJSVpwoQJSkxMVHR0tN5++22tXr1aTZo0kWTN3H+Wn58vSWc8fT86Otqzzhij/v3768EHH1Tbtm21a9euqo5ZYVb8zP/R8ePHNXr0aN19992eLwP11f6m7PhQenq6tmzZ4nUdxkcffaQvv/xSOTk5fkxWvmbNmmnz5s06cuSIPvjgA6WlpWnlypXavn27JXPn5eVp+PDhWrZs2Rn/ipSsvb//+K/zli1bqn379mrUqJHee+89RUVFWTK32+1W27Zt9cwzz0iS2rRpoy1btmjWrFlKS0vTxo0b9cILL2jTpk2Wue6lLK+99ppSUlI85/ut+jl57733NH/+fC1YsEDNmzfX5s2bNWLECMXGxlp+f7/55psaOHCg6tevr8DAQF1zzTW6++67tXHjRkvnvlAvvviifv31V2VkZPg7ygWx6mf+tJMnT6pfv34yxmjmzJmecZ/t7ws66YVypaenmwYNGpgdO3Z4jQ8fPtw4HA4TGBjoWSSZgIAAc9NNN/kn7Fl06dLFDBkyxLK5Fy1aZCSdket01qFDh1oyd3natm1rxowZY9n9HRcXZwYNGuQ19vLLL5vY2FhjjDFTp04tN3ejRo38kPhMu3btMgEBAWbx4sWeMavu7wYNGpiXXnrJa2zChAmmWbNmxpjqsb+Li4vNvn37jDHG9OvXz/To0cOyufWna0h+/vnnMq8HufHGG80jjzxijDGmV69eJiAg4IzfJTAw0Nx3331+yf1H5V2zY4XPfHm5T5w4YXr37m1atmxpfvnlF691vtrfHNmpJGOMhg0bpkWLFmnFihWKj4/3Wj9mzBgNHjzYa6xFixaaOnWqevbsWZVRz4vb7VZJSYmeeuopS+bu0qWLvv/+e6+xAQMGKCEhQaNHj9bll1+uBx54wGu9FXKXpbi4WD///LPuvfde9evXz5L7u2PHjmc8SuGnn35So0aNJEn33nvvGddNdevWTffee68GDBhQZTnPZs6cOapXr55uueUWz5hV/3957NgxBQR4X0oZGBgot9stqXrs79DQUIWGhurw4cP6/PPPNXnyZPXt29fyuSUpPj5eLpdL2dnZat26tSSpqKhIa9eu1UMPPSRJmj59up5++mnPe/bt26du3brp3XffVfv27f0R+7xY9TN/+ojOtm3btHz5ctWtW9drva/2N2WnktLT07VgwQL961//UlhYmOe8bkREhEJCQuRyucq8ECwuLu6MYlTVMjIylJKSori4OP36669asGCBVqxYoc8//9yyucPCwryuh5JO/eFat25dz7gVc0vSY489pp49e6pRo0bat2+fxo0bp8DAQN19992KioqyZO7T5/ifeeYZ9evXT+vWrdPs2bM1e/ZsSVLdunXP+MOpZs2acrlcatasmT8ie3G73ZozZ47S0tJUo8b//8edVT/fPXv21MSJExUXF6fmzZsrJydHU6ZM0cCBAyVZe39//vnnMsaoWbNm2r59u0aNGqWEhAQNGDBANWvWtEzu4uJibd++3fN6586d2rx5syIjIxUXF6cRI0bo6aefVtOmTRUfH6+xY8cqNjZWvXv3lnTqM/JHtWrVkiRdeeWVatCggd9yHzp0SHv27PE8o+b0P1JOf9b99Zk/W+6YmBjdfvvt2rRpkz755BOVlpZ6/g6NjIxUUFCQ7/Z35Q5KQVKZy5w5c876Hn/f4mqMMQMHDjSNGjUyQUFBJioqynTp0sV88cUX5W5vldx/Vtat539kldx33nmniYmJMUFBQaZ+/frmzjvvNNu3by93e6vk/vjjj83VV19tnE6nSUhIMLNnzz7r9la4pfi0zz//3EgyW7duPee2VtjfRUVFZvjw4SYuLs4EBwebK664wvz97383JSUl5b7HKvv73XffNVdccYUJCgoyLpfLpKenm8LCwnK391fu5cuXl/lndlpamjHm1O3nY8eONdHR0cbpdJouXbqc9fNzrlu9qyr3nDlzylw/bty4cuesis/82XKf3ndlLcuXLy9zvorub4cx5TyaEwAAwAZ4zg4AALA1yg4AALA1yg4AALA1yg4AALA1yg4AALA1yg4AALA1yg4AALA1yg4AALA1yg6AStu1a5ccDoc2b958UX/OihUr5HA4VFhYeFF/DgB7oewAOKf+/fvL4XB4lrp166p79+767rvv/JrrdPk5vURHR6tv377asWOHX3MBsBbKDoDz0r17d+3fv1/79+9Xdna2atSooVtvvdXfsSSd+tLDffv26f3339cPP/ygnj17qrS09IztjDH6/fff/ZCwfFbMBNgNZQfAeXE6nZ5vTm7durXGjBmjvLw8HTx4sMztV65cqXbt2snpdComJkZjxozx+ku9pKREjzzyiOrVq6fg4GB16tRJ69ev95rjs88+03/9138pJCREN998s3bt2lXmz6pXr55iYmJ04403KjMzUz/++KO2b9/uOfKzZMkSXXvttXI6nfrqq6/kdruVlZWl+Ph4hYSEqFWrVvrggw888x0+fFipqamKiopSSEiImjZtqjlz5kiSTpw4oaFDhyomJkbBwcFq1KiRsrKyJJV9Oq+wsFAOh0MrVqyQpApnAlBxNfwdAED1U1xcrLfeektNmjRR3bp1dfToUa/1e/fuVY8ePdS/f3/NmzdP//73v3X//fcrODhY//jHPyRJjz/+uD788EO98cYbatSokSZPnqxu3bpp+/btioyMVF5envr06aP09HQNGTJEGzZs0P/7f//vnNlCQkIknSolp40ZM0bPPfecrrjiCtWpU0dZWVl66623NGvWLDVt2lSrVq3SPffco6ioKN10000aO3asfvzxRy1ZskSXX365tm/frt9++02SNH36dH300Ud67733FBcXp7y8POXl5V3wPrzQTAAqoXJf3g7gUpCWlmYCAwNNaGioCQ0NNZJMTEyM2bhxozHGmJ07dxpJJicnxxhjzBNPPGGaNWtm3G63Z44ZM2aYWrVqmdLSUlNcXGxq1qxp5s+f71l/4sQJExsbayZPnmyMMSYjI8NcddVVXjlGjx5tJJnDhw8bY4xZvny51+t9+/aZDh06mPr165uSkhLP+sWLF3vmOH78uLnsssvMN9984zX3oEGDzN13322MMaZnz55mwIABZe6LYcOGmc6dO3v9bqf9eT8YY8zhw4eNJLN8+XKvzBeaCUDFcWQHwHm5+eabNXPmTEmnTvO8/PLLSklJ0bp1687YNjc3V0lJSXI4HJ6xjh07qri4WP/5z39UWFiokydPqmPHjp71NWvWVLt27ZSbm+uZo3379l7zJiUllZmtQYMGMsbo2LFjatWqlT788EMFBQV51rdt29bz39u3b9exY8f017/+1WuOEydOqE2bNpKkhx56SH379tWmTZvUtWtX9e7dWx06dJB06mLtv/71r2rWrJm6d++uW2+9VV27dj33DvyTC80EoOIoOwDOS2hoqJo0aeJ5/eqrryoiIkKvvPKKBg8e7Mdk0v/93/8pPDxc9erVU1hY2BnrQ0NDPf9dXFwsSfr0009Vv359r+2cTqckKSUlRbt379Znn32mZcuWqUuXLkpPT9dzzz2na665Rjt37tSSJUv0v//7v+rXr5+Sk5P1wQcfKCDg1GWQxhjPnCdPniwz84VmAlBxlB0AFeJwOBQQEOC5luWPEhMT9eGHH8oY4zm68/XXXyssLEwNGjRQ3bp1FRQUpK+//lqNGjWSdKoUrF+/XiNGjPDM8dFHH3nNu2bNmjKzxMfHq3bt2ueV+6qrrpLT6dSePXvOei1MVFSU0tLSlJaWphtuuEGjRo3Sc889J0kKDw/XnXfeqTvvvFO33367unfvrkOHDikqKkqStH//fs8RmfN59tD5ZgJQMZQdAOelpKRE+fn5kk6dxnrppZdUXFysnj17nrHtww8/rGnTpmnYsGEaOnSotm7dqnHjxmnkyJEKCAhQaGioHnroIY0aNUqRkZGKi4vT5MmTdezYMQ0aNEiS9OCDD+r555/XqFGjNHjwYG3cuFFz586t9O8RFhamxx57TI8++qjcbrc6deqkI0eO6Ouvv1Z4eLjS0tKUmZmpa6+9Vs2bN1dJSYk++eQTJSYmSpKmTJmimJgYtWnTRgEBAXr//fflcrlUu3ZtBQQE6Prrr9ezzz6r+Ph4HThwQE8++aRPMgGoBD9fMwSgGkhLSzOSPEtYWJi57rrrzAcffGCMKfvC3BUrVpjrrrvOBAUFGZfLZUaPHm1OnjzpWf/bb7+ZYcOGmcsvv9w4nU7TsWNHs27dOq+f+/HHH5smTZoYp9NpbrjhBvP666+f9QLlPytvvdvtNtOmTTPNmjUzNWvWNFFRUaZbt25m5cqVxhhjJkyYYBITE01ISIiJjIw0vXr1Mjt27DDGGDN79mzTunVrExoaasLDw02XLl3Mpk2bPHP/+OOPJikpyYSEhJjWrVubL774oswLlC80E4CKcxjzh5PLAAAANsNDBQEAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK1RdgAAgK39f+ds5odXv36IAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['BloodPressure'],ec='white',color='green', bins=24)\n",
    "plt.xticks(np.arange(df['BloodPressure'].min(),df['BloodPressure'].max()+10,10))\n",
    "plt.ylabel('Density')\n",
    "plt.xlabel('BloodPressure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Insulin'],ec='white',color='green', bins=24)\n",
    "plt.xticks(np.arange(df['Insulin'].min(),df['Insulin'].max(),100))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['DiabetesPedigreeFunction'],ec='white',color='green', bins=24)\n",
    "plt.xticks(np.arange(df['DiabetesPedigreeFunction'].min(),df['DiabetesPedigreeFunction'].max(),0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "normalizing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome', 'AgeGroup'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.00000000e+00, 1.48000000e+02, 7.20000000e+01, 3.50000000e+01,\n",
       "        2.49440000e+02, 3.36000000e+01, 6.27000000e-01, 5.00000000e+01],\n",
       "       [1.00000000e+00, 8.50000000e+01, 6.60000000e+01, 2.90000000e+01,\n",
       "        1.64602564e+02, 2.66000000e+01, 3.51000000e-01, 3.10000000e+01],\n",
       "       [8.00000000e+00, 1.83000000e+02, 6.40000000e+01, 3.06203704e+01,\n",
       "        1.64602564e+02, 2.33000000e+01, 6.72000000e-01, 3.20000000e+01],\n",
       "       [1.00000000e+00, 8.90000000e+01, 6.60000000e+01, 2.30000000e+01,\n",
       "        9.40000000e+01, 2.81000000e+01, 1.67000000e-01, 2.10000000e+01],\n",
       "       [0.00000000e+00, 1.37000000e+02, 4.00000000e+01, 3.50000000e+01,\n",
       "        1.68000000e+02, 4.31000000e+01, 2.28800000e+00, 3.30000000e+01]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data = df[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']].values\n",
    "y_data = df[['Outcome']].values\n",
    "x_data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.63872696,  0.86424691, -0.0308324 ,  0.6301896 ,  0.95370915,\n",
       "         0.16699439,  0.467638  ,  1.42463671],\n",
       "       [-0.84582934, -1.20660676, -0.52580863, -0.04632602,  0.02112676,\n",
       "        -0.85109636, -0.36549383, -0.19177802],\n",
       "       [ 1.23254948,  2.01472117, -0.69080071,  0.13637496,  0.02112676,\n",
       "        -1.33105343,  0.60347471, -0.10670356],\n",
       "       [-0.84582934, -1.07512399, -0.52580863, -0.72284163, -0.75497764,\n",
       "        -0.63293405, -0.92091504, -1.04252262],\n",
       "       [-1.14274059,  0.50266928, -2.67070563,  0.6301896 ,  0.05847335,\n",
       "         1.54868898,  5.4815219 , -0.0216291 ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "scaler = preprocessing.StandardScaler().fit(x_data)\n",
    "x_data = scaler.transform(x_data.astype(float))\n",
    "\n",
    "x_data[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "splitting train and test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test data size:  154\n",
      "train data size:  613\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_data,y_data,train_size=0.8,random_state=1)\n",
    "y_test = np.ravel(y_test)\n",
    "y_train = np.ravel(y_train)\n",
    "print('test data size: ', len(x_test))\n",
    "print('train data size: ', len(x_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "k = 6\n",
    "knn_model = KNeighborsClassifier(n_neighbors=k).fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "predicting y_hat, evaluating model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predict:  [0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0]\n",
      "actual:   [0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "y_hat = knn_model.predict(x_test)\n",
    "\n",
    "print('predict: ',y_hat[0:20])\n",
    "print('actual:  ',y_test[0:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score: 0.7338\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('accuracy score: %.4f' % metrics.accuracy_score(y_test,y_hat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we loop over different Ks to achieve most accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy for k=1 is 0.616883\n",
      "accuracy for k=2 is 0.707792\n",
      "accuracy for k=3 is 0.740260\n",
      "accuracy for k=4 is 0.766234\n",
      "accuracy for k=5 is 0.766234\n",
      "accuracy for k=6 is 0.733766\n",
      "accuracy for k=7 is 0.759740\n",
      "accuracy for k=8 is 0.753247\n",
      "accuracy for k=9 is 0.759740\n"
     ]
    }
   ],
   "source": [
    "acc = np.zeros(9)\n",
    "for k in range(1,10):\n",
    "    knn_model = KNeighborsClassifier(n_neighbors=k).fit(x_train,y_train)\n",
    "    y_hat = knn_model.predict(x_test)\n",
    "    acc[k-1] = metrics.accuracy_score(y_test,y_hat)\n",
    "    print('accuracy for k=%i is %f' %(k,acc[k-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best accuracy(0.7662) was at k=4\n"
     ]
    }
   ],
   "source": [
    "print('best accuracy(%.4f) was at k=%i' %(acc.max(),acc.argmax()+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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