Diabetes Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "61a59435",
   "metadata": {},
   "source": [
    "# Importing required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6d66907e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b3c3abf",
   "metadata": {},
   "source": [
    "# Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68f78d23",
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"diabetes_dataset/diabetes.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a893fd9",
   "metadata": {},
   "source": [
    "# Checking for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8c79992c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b1b2aa",
   "metadata": {},
   "source": [
    "# Co-relation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00d0e530",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
      "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
      "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
      "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
      "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
      "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
      "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
      "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
      "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
      "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
      "\n",
      "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
      "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
      "Glucose                   0.331357  0.221071                  0.137337   \n",
      "BloodPressure             0.088933  0.281805                  0.041265   \n",
      "SkinThickness             0.436783  0.392573                  0.183928   \n",
      "Insulin                   1.000000  0.197859                  0.185071   \n",
      "BMI                       0.197859  1.000000                  0.140647   \n",
      "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
      "Age                      -0.042163  0.036242                  0.033561   \n",
      "Outcome                   0.130548  0.292695                  0.173844   \n",
      "\n",
      "                               Age   Outcome  \n",
      "Pregnancies               0.544341  0.221898  \n",
      "Glucose                   0.263514  0.466581  \n",
      "BloodPressure             0.239528  0.065068  \n",
      "SkinThickness            -0.113970  0.074752  \n",
      "Insulin                  -0.042163  0.130548  \n",
      "BMI                       0.036242  0.292695  \n",
      "DiabetesPedigreeFunction  0.033561  0.173844  \n",
      "Age                       1.000000  0.238356  \n",
      "Outcome                   0.238356  1.000000  \n"
     ]
    }
   ],
   "source": [
    "correlation = data.corr()\n",
    "print(correlation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "319cde35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(correlation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68a19cab",
   "metadata": {},
   "source": [
    "# Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "253ce72c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(\"Outcome\", axis=1)\n",
    "Y = data['Outcome']\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "afb74b3d",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>6</td>\n",
       "      <td>98</td>\n",
       "      <td>58</td>\n",
       "      <td>33</td>\n",
       "      <td>190</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.430</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>75</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>0.148</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.158</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>8</td>\n",
       "      <td>107</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>0.856</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>7</td>\n",
       "      <td>136</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.9</td>\n",
       "      <td>0.210</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>9</td>\n",
       "      <td>165</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.302</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>56</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>33.3</td>\n",
       "      <td>1.251</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>8</td>\n",
       "      <td>95</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.485</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>2</td>\n",
       "      <td>146</td>\n",
       "      <td>70</td>\n",
       "      <td>38</td>\n",
       "      <td>360</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.337</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>8</td>\n",
       "      <td>74</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>49</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.705</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "668            6       98             58             33      190  34.0   \n",
       "324            2      112             75             32        0  35.7   \n",
       "624            2      108             64              0        0  30.8   \n",
       "690            8      107             80              0        0  24.6   \n",
       "473            7      136             90              0        0  29.9   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "355            9      165             88              0        0  30.4   \n",
       "534            1       77             56             30       56  33.3   \n",
       "344            8       95             72              0        0  36.8   \n",
       "296            2      146             70             38      360  28.0   \n",
       "462            8       74             70             40       49  35.3   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "668                     0.430   43  \n",
       "324                     0.148   21  \n",
       "624                     0.158   21  \n",
       "690                     0.856   34  \n",
       "473                     0.210   50  \n",
       "..                        ...  ...  \n",
       "355                     0.302   49  \n",
       "534                     1.251   24  \n",
       "344                     0.485   57  \n",
       "296                     0.337   29  \n",
       "462                     0.705   39  \n",
       "\n",
       "[154 rows x 8 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c33c538",
   "metadata": {},
   "source": [
    "# Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e30f79eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(max_iter=1000),\n",
    "    \"Random Forest\": RandomForestClassifier(),\n",
    "    \"Gradient Boosting\": GradientBoostingClassifier(),\n",
    "    \"SVM\": SVC()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b7c4f696",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 0.7468\n",
      "Random Forest Accuracy: 0.7468\n",
      "Gradient Boosting Accuracy: 0.7468\n",
      "SVM Accuracy: 0.7662\n"
     ]
    }
   ],
   "source": [
    "# Training and evaluating each model\n",
    "for name, model in models.items():\n",
    "    # Train the model\n",
    "    model.fit(X_train, Y_train)\n",
    "    # Make predictions\n",
    "    Y_pred = model.predict(X_test)\n",
    "    # Calculate accuracy\n",
    "    accuracy = accuracy_score(Y_test, Y_pred)\n",
    "    print(f\"{name} Accuracy: {accuracy:.4f}\")"
   ]
  }
 ],
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