Diabetes_prediction_using_machine_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c38811eb",
   "metadata": {},
   "source": [
    "### Importing the Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "689a9b6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import time\n",
    "from sklearn.metrics import accuracy_score,precision_score, recall_score, f1_score\n",
    "\n",
    "import warnings \n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae1bc684",
   "metadata": {},
   "source": [
    "### Data Collection and Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16ebf37e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are working with PIMA Diabetes Dataset\n",
    "# loading the diabetes dataset to a pandas dataframe\n",
    "diabetes_dataset = pd.read_csv(\"diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4a17710",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.read_csv?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24c0a259",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the first 5 rows of the dataset\n",
    "diabetes_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c909724e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of rows and columns in the dataset\n",
    "diabetes_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fd844758",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the statistical measure of data\n",
    "diabetes_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "495943a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome\n",
       "0    500\n",
       "1    268\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_dataset['Outcome'].value_counts()\n",
    "# 0 -> Non-Diabetic Patient\n",
    "# 1 -> Diabetic Patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8181959e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.298000</td>\n",
       "      <td>109.980000</td>\n",
       "      <td>68.184000</td>\n",
       "      <td>19.664000</td>\n",
       "      <td>68.792000</td>\n",
       "      <td>30.304200</td>\n",
       "      <td>0.429734</td>\n",
       "      <td>31.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.865672</td>\n",
       "      <td>141.257463</td>\n",
       "      <td>70.824627</td>\n",
       "      <td>22.164179</td>\n",
       "      <td>100.335821</td>\n",
       "      <td>35.142537</td>\n",
       "      <td>0.550500</td>\n",
       "      <td>37.067164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "Outcome                                                                      \n",
       "0           3.298000  109.980000      68.184000      19.664000   68.792000   \n",
       "1           4.865672  141.257463      70.824627      22.164179  100.335821   \n",
       "\n",
       "               BMI  DiabetesPedigreeFunction        Age  \n",
       "Outcome                                                  \n",
       "0        30.304200                  0.429734  31.190000  \n",
       "1        35.142537                  0.550500  37.067164  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_dataset.groupby('Outcome').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24d12977",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separating the data and labels\n",
    "X = diabetes_dataset.drop(columns='Outcome',axis=1)\n",
    "Y = diabetes_dataset['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "05bf2043",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     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  \n",
      "0                       0.627   50  \n",
      "1                       0.351   31  \n",
      "2                       0.672   32  \n",
      "3                       0.167   21  \n",
      "4                       2.288   33  \n",
      "..                        ...  ...  \n",
      "763                     0.171   63  \n",
      "764                     0.340   27  \n",
      "765                     0.245   30  \n",
      "766                     0.349   47  \n",
      "767                     0.315   23  \n",
      "\n",
      "[768 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f7efe7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      1\n",
      "1      0\n",
      "2      1\n",
      "3      0\n",
      "4      1\n",
      "      ..\n",
      "763    0\n",
      "764    0\n",
      "765    0\n",
      "766    1\n",
      "767    0\n",
      "Name: Outcome, Length: 768, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c49f8abd",
   "metadata": {},
   "source": [
    "### Data Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60cd0282",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "18972921",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "049db714",
   "metadata": {},
   "outputs": [],
   "source": [
    "standardirized_data = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e8c51616",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198\n",
      "   1.4259954 ]\n",
      " [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078\n",
      "  -0.19067191]\n",
      " [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732\n",
      "  -0.10558415]\n",
      " ...\n",
      " [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336\n",
      "  -0.27575966]\n",
      " [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101\n",
      "   1.17073215]\n",
      " [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505\n",
      "  -0.87137393]]\n"
     ]
    }
   ],
   "source": [
    "print(standardirized_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fb5360cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = standardirized_data\n",
    "Y = diabetes_dataset['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c76e0f84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198\n",
      "   1.4259954 ]\n",
      " [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078\n",
      "  -0.19067191]\n",
      " [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732\n",
      "  -0.10558415]\n",
      " ...\n",
      " [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336\n",
      "  -0.27575966]\n",
      " [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101\n",
      "   1.17073215]\n",
      " [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505\n",
      "  -0.87137393]]\n",
      "0      1\n",
      "1      0\n",
      "2      1\n",
      "3      0\n",
      "4      1\n",
      "      ..\n",
      "763    0\n",
      "764    0\n",
      "765    0\n",
      "766    1\n",
      "767    0\n",
      "Name: Outcome, Length: 768, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a83a2e",
   "metadata": {},
   "source": [
    "### Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c2c8813c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y , random_state=104,test_size=0.15, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5d0f4c72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 8) (652, 8) (116, 8)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape,X_train.shape,X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93c185c9",
   "metadata": {},
   "source": [
    "### Training the model and Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88349303",
   "metadata": {},
   "source": [
    "### Support Vector Machine Classifier (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "22a11a96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 0.020946264266967773\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "begin = time.time()\n",
    "classifier1 = svm.SVC(kernel='linear',C=1.0, random_state=42)\n",
    "\n",
    "# Training the Support Vector Machine Classifier\n",
    "classifier1.fit(X_train,Y_train)\n",
    "\n",
    "end = time.time()\n",
    "print(\"Execution time:\",end - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "58bd79ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data: 0.7791411042944786\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the training data\n",
    "X_train_prediction = classifier1.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction,Y_train)\n",
    "print('Accuracy score of training data:',training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c37cb022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of testing data: 0.8275862068965517\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the testing data\n",
    "X_test_prediction = classifier1.predict(X_test)\n",
    "testing_data_accuracy = accuracy_score(X_test_prediction,Y_test)\n",
    "print('Accuracy score of testing data:',testing_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a6bbe10d-f198-4e49-910a-a60ed632d0fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.5675675675675675\n",
      "Recall: 0.84\n",
      "F1-score: 0.6774193548387097\n"
     ]
    }
   ],
   "source": [
    "print('Precision:',precision_score(X_test_prediction,Y_test))\n",
    "print('Recall:',recall_score(X_test_prediction,Y_test))\n",
    "print('F1-score:',f1_score(X_test_prediction,Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1670bb49",
   "metadata": {},
   "source": [
    "### Random Forest Classifier (RF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2253d16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 0.06460285186767578\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "begin = time.time()\n",
    "classifier2 = RandomForestClassifier(max_depth=2,n_estimators=30,min_samples_split=5,max_leaf_nodes=6,random_state=22)\n",
    "\n",
    "# Training the Random Forest Classifier\n",
    "classifier2.fit(X_train,Y_train)\n",
    "\n",
    "end = time.time()\n",
    "print(\"Execution time:\",end - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "311d803b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data: 0.7668711656441718\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the training data\n",
    "X_train_prediction = classifier2.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction,Y_train)\n",
    "print('Accuracy score of training data:',training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9b0c8371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of testing data: 0.7931034482758621\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the testing data\n",
    "X_test_prediction = classifier2.predict(X_test)\n",
    "testing_data_accuracy = accuracy_score(X_test_prediction,Y_test)\n",
    "print('Accuracy score of testing data:',testing_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3e646bb1-7fc2-45cf-8d1b-7ded6f23e389",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.43243243243243246\n",
      "Recall: 0.8421052631578947\n",
      "F1-score: 0.5714285714285715\n"
     ]
    }
   ],
   "source": [
    "print('Precision:',precision_score(X_test_prediction,Y_test))\n",
    "print('Recall:',recall_score(X_test_prediction,Y_test))\n",
    "print('F1-score:',f1_score(X_test_prediction,Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eeffd4a",
   "metadata": {},
   "source": [
    "### Logistic Regression (LR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1910dd22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 0.0049893856048583984\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "begin = time.time()\n",
    "classifier3 = LogisticRegression()\n",
    "\n",
    "# Training the Logisitc Regression\n",
    "classifier3.fit(X_train,Y_train)\n",
    "\n",
    "end = time.time()\n",
    "print(\"Execution time:\",end - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f50015fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data: 0.7714723926380368\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the training data\n",
    "X_train_prediction = classifier3.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction,Y_train)\n",
    "print('Accuracy score of training data:',training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "494e03d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of testing data: 0.8362068965517241\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the testing data\n",
    "X_test_prediction = classifier3.predict(X_test)\n",
    "testing_data_accuracy = accuracy_score(X_test_prediction,Y_test)\n",
    "print('Accuracy score of testing data:',testing_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ceefc51c-13f3-48d6-9252-5a250404a516",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.5945945945945946\n",
      "Recall: 0.8461538461538461\n",
      "F1-score: 0.6984126984126985\n"
     ]
    }
   ],
   "source": [
    "print('Precision:',precision_score(X_test_prediction,Y_test))\n",
    "print('Recall:',recall_score(X_test_prediction,Y_test))\n",
    "print('F1-score:',f1_score(X_test_prediction,Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bf98508",
   "metadata": {},
   "source": [
    "### Logistic Regression (LR) with Cross-Validation (CV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "640006f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 0.44257497787475586\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "begin = time.time()\n",
    "classifier4 = LogisticRegressionCV(cv=5)\n",
    "\n",
    "# Training the Logisitc Regression with Cross-Validation\n",
    "classifier4.fit(X_train,Y_train)\n",
    "\n",
    "end = time.time()\n",
    "print(\"Execution time:\",end - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "80101c5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data: 0.7699386503067485\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the training data\n",
    "X_train_prediction = classifier4.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction,Y_train)\n",
    "print('Accuracy score of training data:',training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fb6075f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of testing data: 0.8362068965517241\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the testing data\n",
    "X_test_prediction = classifier4.predict(X_test)\n",
    "testing_data_accuracy = accuracy_score(X_test_prediction,Y_test)\n",
    "print('Accuracy score of testing data:',testing_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f306d8",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier (DT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c7bef1f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 0.003838777542114258\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "begin = time.time()\n",
    "classifier5 = DecisionTreeClassifier(criterion = \"gini\",random_state = 20,max_depth=3, min_samples_leaf=4)\n",
    "\n",
    "# Training the Decision Tree Classifier\n",
    "classifier5.fit(X_train,Y_train)\n",
    "\n",
    "end = time.time()\n",
    "print(\"Execution time:\",end - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9dd7a0c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data: 0.7776073619631901\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the training data\n",
    "X_train_prediction = classifier5.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction,Y_train)\n",
    "print('Accuracy score of training data:',training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c4495f85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of testing data: 0.7586206896551724\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the testing data\n",
    "X_test_prediction = classifier5.predict(X_test)\n",
    "testing_data_accuracy = accuracy_score(X_test_prediction,Y_test)\n",
    "print('Accuracy score of testing data:',testing_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "479af3ca-42c9-46fb-bd3a-1c3b111545c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.4864864864864865\n",
      "Recall: 0.6666666666666666\n",
      "F1-score: 0.5625\n"
     ]
    }
   ],
   "source": [
    "print('Precision:',precision_score(X_test_prediction,Y_test))\n",
    "print('Recall:',recall_score(X_test_prediction,Y_test))\n",
    "print('F1-score:',f1_score(X_test_prediction,Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dbce998",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors (KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "37703570",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 0.003987550735473633\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "begin = time.time()\n",
    "classifier6 = KNeighborsClassifier(n_neighbors=3, metric='manhattan', p=2 )  \n",
    "\n",
    "#training the K-Nearest Neighbors\n",
    "classifier6.fit(X_train,Y_train)\n",
    "\n",
    "end = time.time()\n",
    "print(\"Execution time:\",end - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1ed21575",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data: 0.8358895705521472\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the training data\n",
    "X_train_prediction = classifier6.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction,Y_train)\n",
    "print('Accuracy score of training data:',training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2f627722",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of testing data: 0.7931034482758621\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the testing data\n",
    "X_test_prediction = classifier6.predict(X_test)\n",
    "testing_data_accuracy = accuracy_score(X_test_prediction,Y_test)\n",
    "print('Accuracy score of testing data:',testing_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9c985073-1fcc-44fe-b3e4-883fa7649b28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.5945945945945946\n",
      "Recall: 0.7096774193548387\n",
      "F1-score: 0.6470588235294118\n"
     ]
    }
   ],
   "source": [
    "print('Precision:',precision_score(X_test_prediction,Y_test))\n",
    "print('Recall:',recall_score(X_test_prediction,Y_test))\n",
    "print('F1-score:',f1_score(X_test_prediction,Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f750ca4c",
   "metadata": {},
   "source": [
    "### XGBoost Classifier (XGB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "186f9fda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 0.16205668449401855\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "begin = time.time()\n",
    "classifier7 = XGBClassifier(eta=0.1,gamma=10)\n",
    "\n",
    "# Training the XGBoost Classifier\n",
    "classifier7.fit(X_train,Y_train)\n",
    "\n",
    "end = time.time()\n",
    "print(\"Execution time:\",end - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fc378b02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data: 0.7822085889570553\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the training data\n",
    "X_train_prediction = classifier7.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction,Y_train)\n",
    "print('Accuracy score of training data:',training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "20e97246",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of testing data: 0.7931034482758621\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the testing data\n",
    "X_test_prediction = classifier7.predict(X_test)\n",
    "testing_data_accuracy = accuracy_score(X_test_prediction,Y_test)\n",
    "print('Accuracy score of testing data:',testing_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "591238f0-58f7-4f1b-942e-ecb8ca97d06e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.4864864864864865\n",
      "Recall: 0.782608695652174\n",
      "F1-score: 0.6000000000000001\n"
     ]
    }
   ],
   "source": [
    "print('Precision:',precision_score(X_test_prediction,Y_test))\n",
    "print('Recall:',recall_score(X_test_prediction,Y_test))\n",
    "print('F1-score:',f1_score(X_test_prediction,Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f514a3e5-3ff9-49b7-a405-6eb422817118",
   "metadata": {},
   "source": [
    "### Naive Bayes Classifier (NB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "789c0a6c-9428-45ab-a40e-e9289b9a39d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 0.0015261173248291016\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "classifier8 = GaussianNB()\n",
    "begin = time.time()\n",
    "\n",
    "# Training the Naive Bayes Classifier\n",
    "classifier8.fit(X_train,Y_train)\n",
    "\n",
    "end = time.time()\n",
    "print(\"Execution time:\",end - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "db57f489-5659-490c-a57c-1ccef1e33b28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data: 0.7576687116564417\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the training data\n",
    "X_train_prediction = classifier8.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction,Y_train)\n",
    "print('Accuracy score of training data:',training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5162080d-e5e6-40d3-b22f-647812b6280f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of testing data: 0.7844827586206896\n"
     ]
    }
   ],
   "source": [
    "# Accuracy score on the testing data\n",
    "X_test_prediction = classifier8.predict(X_test)\n",
    "testing_data_accuracy = accuracy_score(X_test_prediction,Y_test)\n",
    "print('Accuracy score of testing data:',testing_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "17678f55-61d6-4880-be66-d3fbad0224a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.5675675675675675\n",
      "Recall: 0.7\n",
      "F1-score: 0.626865671641791\n"
     ]
    }
   ],
   "source": [
    "print('Precision:',precision_score(X_test_prediction,Y_test))\n",
    "print('Recall:',recall_score(X_test_prediction,Y_test))\n",
    "print('F1-score:',f1_score(X_test_prediction,Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a8c3ed1",
   "metadata": {},
   "source": [
    "### Making a Predictive System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b80065ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.84488505 -0.99820778 -0.16054575  0.15453319  0.12330164 -0.49404308\n",
      "  -0.92076261 -1.04154944]]\n",
      "[0]\n",
      "The person is not diabetic\n"
     ]
    }
   ],
   "source": [
    "# input_data = (6,148,72,35,0,33.6,0.627,50) => 1 (Diabetic)\n",
    "\n",
    "input_data = (1, 89, 66, 23, 94, 28.1, 0.167, 21) # => 0 (Non-Diabetic)\n",
    "\n",
    "# Changing the input data to a numpy array\n",
    "input_data_as_numpy_array = np.asarray(input_data)\n",
    "\n",
    "# Reshaping the array as the prediction is done for one instance\n",
    "input_data_reshape = input_data_as_numpy_array.reshape(1,-1)\n",
    "\n",
    "# Standardize the input data\n",
    "std_data = scaler.transform(input_data_reshape)\n",
    "print(std_data)\n",
    "\n",
    "prediction = classifier3.predict(std_data)\n",
    "print(prediction)\n",
    "\n",
    "if(prediction[0]==0):\n",
    "    print(\"The person is not diabetic.\");\n",
    "else:\n",
    "    print(\"The person is diabetic.\");"
   ]
  }
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