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diabetes_model.ipynb
812 lines (811 with data), 34.3 kB
{
"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": {
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"<div>\n",
"<style scoped>\n",
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" 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",
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" <td>33.6</td>\n",
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" <td>28.1</td>\n",
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" <th>4</th>\n",
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" <td>180</td>\n",
" <td>32.9</td>\n",
" <td>0.171</td>\n",
" <td>63</td>\n",
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" <tr>\n",
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" <td>0.340</td>\n",
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" </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",
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"</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": {
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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='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": []
}
],
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"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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