{

 "cells": [

  {

   "cell_type": "markdown",

   "id": "9af65cb9-8a84-47e4-8bea-1547afe46a15",

   "metadata": {},

   "source": [

    "DIABETIES"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 2,

   "id": "3fba3c9b-5e48-4771-a28a-4ec54fc4bc1b",

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "Pregnancies                 0\n",

       "Glucose                     0\n",

       "BloodPressure               0\n",

       "SkinThickness               0\n",

       "Insulin                     0\n",

       "BMI                         0\n",

       "DiabetesPedigreeFunction    0\n",

       "Age                         0\n",

       "Outcome                     0\n",

       "dtype: int64"

      ]

     },

     "execution_count": 2,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "import numpy as np\n",

    "import pandas as pd\n",

    "from sklearn.model_selection import train_test_split\n",

    "from sklearn import svm\n",

    "from sklearn.metrics import accuracy_score\n",

    "dataset= pd.read_csv(r'C:\\Users\\Pranshu Saini\\Desktop\\disease-prediction-main\\docpat\\datasets\\diabetes.csv')\n",

    "dataset.head(5)\n",

    "dataset.isna().sum()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 4,

   "id": "e99dd297-c606-4501-80c8-fa87d89fc237",

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "The reduced dataframe has 9 columns.\n"

     ]

    }

   ],

   "source": [

    "# removing highly correlated features\n",

    "\n",

    "corr_matrix = dataset.corr().abs() \n",

    "\n",

    "mask = np.triu(np.ones_like(corr_matrix, dtype = bool))\n",

    "tri_df = corr_matrix.mask(mask)\n",

    "\n",

    "to_drop = [x for x in tri_df.columns if any(tri_df[x] > 0.92)]\n",

    "\n",

    "df = dataset.drop(to_drop, axis = 1)\n",

    "\n",

    "print(f\"The reduced dataframe has {df.shape[1]} columns.\")"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 5,

   "id": "64693a8e",

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/html": [

       "<div>\n",

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       "    .dataframe tbody tr th:only-of-type {\n",

       "        vertical-align: middle;\n",

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       "\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",

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       "    <tr>\n",

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       "      <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",

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       "    <tr>\n",

       "      <th>2</th>\n",

       "      <td>8</td>\n",

       "      <td>183</td>\n",

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       "      <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",

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       "    <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",

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       "      <td>101</td>\n",

       "      <td>76</td>\n",

       "      <td>48</td>\n",

       "      <td>180</td>\n",

       "      <td>32.9</td>\n",

       "      <td>0.171</td>\n",

       "      <td>63</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>764</th>\n",

       "      <td>2</td>\n",

       "      <td>122</td>\n",

       "      <td>70</td>\n",

       "      <td>27</td>\n",

       "      <td>0</td>\n",

       "      <td>36.8</td>\n",

       "      <td>0.340</td>\n",

       "      <td>27</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>765</th>\n",

       "      <td>5</td>\n",

       "      <td>121</td>\n",

       "      <td>72</td>\n",

       "      <td>23</td>\n",

       "      <td>112</td>\n",

       "      <td>26.2</td>\n",

       "      <td>0.245</td>\n",

       "      <td>30</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>766</th>\n",

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       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>30.1</td>\n",

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       "      <td>47</td>\n",

       "      <td>1</td>\n",

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       "    <tr>\n",

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       "      <td>93</td>\n",

       "      <td>70</td>\n",

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       "      <td>0</td>\n",

       "      <td>30.4</td>\n",

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       "      <td>23</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

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       "<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": 5,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "df"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "ecc668ff-a516-4ee6-8b2f-76b07d9df34f",

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "(768, 8) (614, 8) (154, 8)\n"

     ]

    }

   ],

   "source": [

    "A= dataset.drop(columns = 'Outcome', axis=1)\n",

    "B= dataset['Outcome']\n",

    "A_training, A_testing, B_training, B_testing = train_test_split(A,B, test_size = 0.2, stratify=B, random_state=5)\n",

    "print(A.shape, A_training.shape, A_testing.shape)\n"

   ]

  },

  {

   "cell_type": "markdown",

   "id": "62af783b-d901-479d-bf3f-512a897fceaa",

   "metadata": {},

   "source": [

    "LogisticRegression"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "69152bd3-c30b-46b4-bf8e-32dbcd33e163",

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "0.7817589576547231\n",

      "0.7532467532467533\n"

     ]

    },

    {

     "name": "stderr",

     "output_type": "stream",

     "text": [

      "c:\\Users\\Dell\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",

      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",

      "\n",

      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",

      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",

      "Please also refer to the documentation for alternative solver options:\n",

      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",

      "  n_iter_i = _check_optimize_result(\n"

     ]

    }

   ],

   "source": [

    "# fitting data to model\n",

    "\n",

    "from sklearn.linear_model import LogisticRegression\n",

    "\n",

    "log_reg = LogisticRegression()\n",

    "log_reg.fit(A_training, B_training)\n",

    "B_pred = log_reg.predict(A_testing)\n",

    "# accuracy score\n",

    "\n",

    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",

    "\n",

    "print(accuracy_score(B_training, log_reg.predict(A_training)))\n",

    "\n",

    "log_reg_acc = accuracy_score(B_testing, log_reg.predict(A_testing))\n",

    "print(log_reg_acc)"

   ]

  },

  {

   "cell_type": "markdown",

   "id": "90967102-86d1-4939-9113-4d06ce5bb054",

   "metadata": {},

   "source": [

    "K Neighbors Classifier (KNN)\n"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "2092a5bc-4602-4aa6-adb3-34ee677f8134",

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "0.7980456026058632\n",

      "0.7142857142857143\n"

     ]

    }

   ],

   "source": [

    "from sklearn.neighbors import KNeighborsClassifier\n",

    "\n",

    "knn = KNeighborsClassifier()\n",

    "knn.fit(A_training, B_training)\n",

    "# model predictions \n",

    "\n",

    "B_pred = knn.predict(A_testing)\n",

    "# accuracy score\n",

    "\n",

    "print(accuracy_score(B_training, knn.predict(A_training)))\n",

    "\n",

    "knn_acc = accuracy_score(B_testing, knn.predict(A_testing))\n",

    "print(knn_acc)"

   ]

  },

  {

   "cell_type": "markdown",

   "id": "da68f26d-4291-4076-bf2c-f20dd524c6e7",

   "metadata": {},

   "source": [

    "Support Vector Machine (SVM)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "a27c4a3c-15e7-492d-88a6-526ddfc968cd",

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "{'C': 1, 'gamma': 0.0001}"

      ]

     },

     "execution_count": 8,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "from sklearn.svm import SVC\n",

    "from sklearn.model_selection import GridSearchCV\n",

    "\n",

    "svc = SVC(probability=True)\n",

    "parameters = {\n",

    "    'gamma' : [0.0001, 0.001, 0.01, 0.1],\n",

    "    'C' : [0.01, 0.05, 0.5, 0.1, 1, 10, 15, 20]\n",

    "}\n",

    "\n",

    "grid_search = GridSearchCV(svc, parameters)\n",

    "grid_search.fit(A_training, B_training)\n",

    "# best parameters\n",

    "\n",

    "grid_search.best_params_\n",

    "\n"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "d8da6a6f-eba3-4a66-b61f-04a58313de41",

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "0.7557643609222977"

      ]

     },

     "execution_count": 9,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "# best score \n",

    "\n",

    "grid_search.best_score_"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "0627d35c-f004-499b-be77-0fe0380aa002",

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "1.0\n",

      "0.6428571428571429\n",

      "              precision    recall  f1-score   support\n",

      "\n",

      "           0       0.66      0.93      0.77       100\n",

      "           1       0.46      0.11      0.18        54\n",

      "\n",

      "    accuracy                           0.64       154\n",

      "   macro avg       0.56      0.52      0.48       154\n",

      "weighted avg       0.59      0.64      0.56       154\n",

      "\n"

     ]

    }

   ],

   "source": [

    "svc = SVC(C = 10, gamma = 0.01, probability=True)\n",

    "svc.fit(A_training, B_training)\n",

    "# model predictions \n",

    "\n",

    "B_pred = svc.predict(A_testing)\n",

    "# accuracy score\n",

    "\n",

    "print(accuracy_score(B_training, svc.predict(A_training)))\n",

    "\n",

    "svc_acc = accuracy_score(B_testing, svc.predict(A_testing))\n",

    "print(svc_acc)\n",

    "# classification report\n",

    "\n",

    "print(classification_report(B_testing, B_pred))"

   ]

  },

  {

   "cell_type": "markdown",

   "id": "e4b77944-3ceb-4b80-a738-e8e5a99e009d",

   "metadata": {},

   "source": [

    "DECISION TREE"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "fe80f32d-ba91-4297-a9d7-6232a48b434c",

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Fitting 5 folds for each of 8640 candidates, totalling 43200 fits\n"

     ]

    },

    {

     "data": {

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       "             param_grid={'criterion': ['gini', 'entropy'],\n",

       "                         'max_depth': range(2, 32),\n",

       "                         'min_samples_leaf': range(1, 10),\n",

       "                         'min_samples_split': range(2, 10),\n",

       "                         'splitter': ['best', 'random']},\n",

       "             verbose=1)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",

       "             param_grid={'criterion': ['gini', 'entropy'],\n",

       "                         'max_depth': range(2, 32),\n",

       "                         'min_samples_leaf': range(1, 10),\n",

       "                         'min_samples_split': range(2, 10),\n",

       "                         'splitter': ['best', 'random']},\n",

       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"

      ],

      "text/plain": [

       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",

       "             param_grid={'criterion': ['gini', 'entropy'],\n",

       "                         'max_depth': range(2, 32),\n",

       "                         'min_samples_leaf': range(1, 10),\n",

       "                         'min_samples_split': range(2, 10),\n",

       "                         'splitter': ['best', 'random']},\n",

       "             verbose=1)"

      ]

     },

     "execution_count": 11,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "from sklearn.tree import DecisionTreeClassifier\n",

    "\n",

    "dtc = DecisionTreeClassifier()\n",

    "\n",

    "parameters = {\n",

    "    'criterion' : ['gini', 'entropy'],\n",

    "    'max_depth' : range(2, 32, 1),\n",

    "    'min_samples_leaf' : range(1, 10, 1),\n",

    "    'min_samples_split' : range(2, 10, 1),\n",

    "    'splitter' : ['best', 'random']\n",

    "}\n",

    "\n",

    "grid_search_dt = GridSearchCV(dtc, parameters, cv = 5, n_jobs = -1, verbose = 1)\n",

    "grid_search_dt.fit(A_training, B_training)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "3500946f-0e9a-4d31-b076-35c851e1ca69",

   "metadata": {},

   "outputs": [

    {

     "data": {

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       "                       min_samples_split=6, splitter=&#x27;random&#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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=19, min_samples_leaf=4,\n",

       "                       min_samples_split=6, splitter=&#x27;random&#x27;)</pre></div></div></div></div></div>"

      ],

      "text/plain": [

       "DecisionTreeClassifier(criterion='entropy', max_depth=19, min_samples_leaf=4,\n",

       "                       min_samples_split=6, splitter='random')"

      ]

     },

     "execution_count": 12,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "# best score\n",

    "\n",

    "grid_search_dt.best_score_\n",

    "dtc = DecisionTreeClassifier(criterion= 'entropy', max_depth= 19, min_samples_leaf= 4, min_samples_split= 6, splitter= 'random')\n",

    "dtc.fit(A_training, B_training)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "73fa053a-be12-46d6-9315-fb8d5d557b7c",

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "0.8224755700325733\n",

      "0.6883116883116883\n"

     ]

    }

   ],

   "source": [

    "B_pred = dtc.predict(A_testing)\n",

    "# accuracy score\n",

    "\n",

    "print(accuracy_score(B_training, dtc.predict(A_training)))\n",

    "\n",

    "dtc_acc = accuracy_score(B_testing, dtc.predict(A_testing))\n",

    "print(dtc_acc)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "f7f0b47c-f612-4fb1-b06f-9c074da06bb7",

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "              precision    recall  f1-score   support\n",

      "\n",

      "           0       0.74      0.80      0.77       100\n",

      "           1       0.57      0.48      0.52        54\n",

      "\n",

      "    accuracy                           0.69       154\n",

      "   macro avg       0.65      0.64      0.64       154\n",

      "weighted avg       0.68      0.69      0.68       154\n",

      "\n"

     ]

    }

   ],

   "source": [

    "# classification report\n",

    "\n",

    "print(classification_report(B_testing, B_pred))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "55fde2ec-b707-47d5-8556-6b461a71f5dd",

   "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>Model</th>\n",

       "      <th>Score</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>0</th>\n",

       "      <td>Logistic Regression</td>\n",

       "      <td>75.32</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>1</th>\n",

       "      <td>KNN</td>\n",

       "      <td>71.43</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>3</th>\n",

       "      <td>Decision Tree Classifier</td>\n",

       "      <td>68.83</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2</th>\n",

       "      <td>SVM</td>\n",

       "      <td>64.29</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                      Model  Score\n",

       "0       Logistic Regression  75.32\n",

       "1                       KNN  71.43\n",

       "3  Decision Tree Classifier  68.83\n",

       "2                       SVM  64.29"

      ]

     },

     "execution_count": 15,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "models = pd.DataFrame({\n",

    "    'Model': ['Logistic Regression', 'KNN', 'SVM', 'Decision Tree Classifier'],\n",

    "    'Score': [100*round(log_reg_acc,4), 100*round(knn_acc,4), 100*round(svc_acc,4), 100*round(dtc_acc,4)]\n",

    "})\n",

    "models.sort_values(by = 'Score', ascending = False)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "24684911-cbad-474c-8743-fcf517c7e01c",

   "metadata": {},

   "outputs": [],

   "source": []

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "41401323-1a4c-4091-8bc2-7797137c0f65",

   "metadata": {},

   "outputs": [],

   "source": [

    "import pickle\n",

    "filename = 'C:/Users/Dell/OneDrive/Desktop/DM PROJECT/diabetes_model.pkl'\n",

    "pickle.dump(log_reg, open(filename, 'wb'))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "b1b7f82b-c26e-437a-8a01-c2bfd34e7268",

   "metadata": {},

   "outputs": [],

   "source": [

    "'''import pickle\n",

    "def load_model(path):\n",

    "    with open(path, 'rb') as file:\n",

    "        model = pickle.load(file)\n",

    "diabetes_model = load_model(r'C:\\Users\\DELL\\Desktop\\app\\diabetes_model.pkl')\n",

    "def predict(inputs):\n",

    "    return diabetes_model.predict(inputs)'''"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "2ef4b8e2-d3d0-4a14-b512-c618d848c8d8",

   "metadata": {},

   "outputs": [],

   "source": []

  }

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