Diabetes Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4abcb617",
   "metadata": {},
   "source": [
    "# Diabetes Prediction using Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a3463b",
   "metadata": {},
   "source": [
    "## Importing the dependencies/libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e96b21e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88fbb937",
   "metadata": {},
   "source": [
    "## Importint Dataset and Performing exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9155f25d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing PIMA Dataset to a pandas dataset\n",
    "diabetes = pd.read_csv('diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3a9ff010",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the first five records\n",
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f2442c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the number of records and attributes in this dataset \n",
    "diabetes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a6fcd78e",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "db443ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the number of diabetic and non-diabetic patients in the dataset\n",
    "# 1 means diabetic and 0 means non-diabetic\n",
    "diabetes['Outcome'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ad607113",
   "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",
       "    </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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.groupby('Outcome').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9f8cdbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperating data and labels\n",
    "X = diabetes.drop(columns = 'Outcome',axis = 1)\n",
    "Y = diabetes['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca6bd481",
   "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": 10,
   "id": "5d40f971",
   "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": "c29f01be",
   "metadata": {},
   "source": [
    "## Performing data standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b582ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating an instance of StandardScaler dependency\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1fcf5b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "standardized_data = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3a8f5caf",
   "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": [
    "# Printing the standardized data\n",
    "print(standardized_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d58decaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X represents the data\n",
    "X = standardized_data\n",
    "\n",
    "# Y represents the model\n",
    "Y = diabetes['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "45767dcc",
   "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(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eb77f0b6",
   "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": "14c376ea",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ce43c2de",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2,stratify=Y,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f62b18bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 8) (614, 8) (154, 8)\n"
     ]
    }
   ],
   "source": [
    "# Checking the size of train test data\n",
    "print(X.shape, X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b88573ba",
   "metadata": {},
   "source": [
    "## Training the model using a Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9d43461b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating an instance of support vector classifier\n",
    "classifier = svm.SVC(kernel = 'linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "155d01de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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>SVC(kernel=&#x27;linear&#x27;)</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\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the Support Vector Machine Classifier\n",
    "classifier.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "037662f0",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "76084fcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy score on training data\n",
    "X_train_prediction = classifier.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "86547584",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data :-  0.7866449511400652\n"
     ]
    }
   ],
   "source": [
    "# Displaying the accuracy score on training data\n",
    "print(\"Accuracy score of training data :- \", training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "36d20f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy score on test data\n",
    "X_test_prediction = classifier.predict(X_test)\n",
    "testing_data_accuracy = accuracy_score(X_test_prediction,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4b92479f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of testing data :-  0.7727272727272727\n"
     ]
    }
   ],
   "source": [
    "# Displaying the accuracy score on test data\n",
    "print(\"Accuracy score of testing data :- \", testing_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4a9be4de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying the Confusion Matrix\n",
    "cm = confusion_matrix(Y_test, X_test_prediction)\n",
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5a2c5ea9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.91      0.84       100\n",
      "           1       0.76      0.52      0.62        54\n",
      "\n",
      "    accuracy                           0.77       154\n",
      "   macro avg       0.77      0.71      0.73       154\n",
      "weighted avg       0.77      0.77      0.76       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# displaying the model's performance\n",
    "print(classification_report(Y_test, X_test_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bae8d8d",
   "metadata": {},
   "source": [
    "## Building a Predictive System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1616f371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The person is not diabetic\n"
     ]
    }
   ],
   "source": [
    "input_data = (1,97,66,15,140,23.2,0.487,22)\n",
    "input_np = np.asarray(input_data)\n",
    "input_reshaped = input_np.reshape(1, -1)\n",
    "\n",
    "#standardizing the input data\n",
    "std_data = scaler.fit_transform(input_reshaped)\n",
    "\n",
    "\n",
    "prediction = classifier.predict(std_data)\n",
    "\n",
    "# printing the prediction\n",
    "if (prediction[0] == 0):\n",
    "    print(\"The person is not diabetic\")\n",
    "else:\n",
    "    print(\"The person is diabetic\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a5e4453",
   "metadata": {},
   "source": [
    "## Saving the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6b80ce37",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5cf36b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'diabetes.sav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4f16c593",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(classifier, open(filename , 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea0610fa",
   "metadata": {},
   "source": [
    "# Made By :-\n",
    "\n",
    "## Manav Malhotra"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}