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--- a +++ b/diabetes_model.ipynb @@ -0,0 +1,811 @@ +{ + "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", + "<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", + " 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<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": 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": { + "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>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><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": { + "text/html": [ + "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion='entropy', max_depth=19, min_samples_leaf=4,\n", + " min_samples_split=6, splitter='random')</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='entropy', max_depth=19, min_samples_leaf=4,\n", + " min_samples_split=6, splitter='random')</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": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}