{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "85536a33", "metadata": {}, "outputs": [], "source": [ "#import pandas library\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 6, "id": "3f1287e0", "metadata": {}, "outputs": [], "source": [ "#Load the uploaded file \n", "df = pd.read_csv('diabetes.csv')" ] }, { "cell_type": "code", "execution_count": 7, "id": "dbe99527", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "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": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 8, "id": "333211c0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
count768.000000768.000000768.000000768.000000768.000000768.000000768.000000768.000000768.000000
mean3.845052120.89453169.10546920.53645879.79947931.9925780.47187633.2408850.348958
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max17.000000199.000000122.00000099.000000846.00000067.1000002.42000081.0000001.000000
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" ], "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": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 9, "id": "07f634ff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 768 entries, 0 to 767\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Pregnancies 768 non-null int64 \n", " 1 Glucose 768 non-null int64 \n", " 2 BloodPressure 768 non-null int64 \n", " 3 SkinThickness 768 non-null int64 \n", " 4 Insulin 768 non-null int64 \n", " 5 BMI 768 non-null float64\n", " 6 DiabetesPedigreeFunction 768 non-null float64\n", " 7 Age 768 non-null int64 \n", " 8 Outcome 768 non-null int64 \n", "dtypes: float64(2), int64(7)\n", "memory usage: 54.1 KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 10, "id": "e7606a45", "metadata": {}, "outputs": [], "source": [ "#import numpy library to process the missing value\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 11, "id": "9f1d5a83", "metadata": {}, "outputs": [], "source": [ "#Define columns that has zero value \n", "cols_with_missing = ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']" ] }, { "cell_type": "code", "execution_count": 15, "id": "1344472b", "metadata": {}, "outputs": [], "source": [ "#replace z value with null value \n", "df[cols_with_missing] = df[cols_with_missing].replace(0,np.nan)" ] }, { "cell_type": "code", "execution_count": 16, "id": "c4f5da91", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 768 entries, 0 to 767\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Pregnancies 768 non-null int64 \n", " 1 Glucose 763 non-null float64\n", " 2 BloodPressure 733 non-null float64\n", " 3 SkinThickness 541 non-null float64\n", " 4 Insulin 394 non-null float64\n", " 5 BMI 757 non-null float64\n", " 6 DiabetesPedigreeFunction 768 non-null float64\n", " 7 Age 768 non-null int64 \n", " 8 Outcome 768 non-null int64 \n", "dtypes: float64(6), int64(3)\n", "memory usage: 54.1 KB\n" ] }, { "data": { "text/plain": [ "(None,\n", " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", " 0 6 148.0 72.0 35.0 NaN 33.6 \n", " 1 1 85.0 66.0 29.0 NaN 26.6 \n", " 2 8 183.0 64.0 NaN NaN 23.3 \n", " 3 1 89.0 66.0 23.0 94.0 28.1 \n", " 4 0 137.0 40.0 35.0 168.0 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": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.info() ,df.head()" ] }, { "cell_type": "code", "execution_count": 17, "id": "f464ecf6", "metadata": {}, "outputs": [], "source": [ "df_filled = df.copy()" ] }, { "cell_type": "code", "execution_count": 20, "id": "45526f6d", "metadata": {}, "outputs": [], "source": [ "#replace nullvalue with median value \n", "for col in cols_with_missing:\n", " median = df_filled[col].median()\n", " df_filled[col] = df_filled[col].fillna(median)" ] }, { "cell_type": "code", "execution_count": 21, "id": "6eef7b67", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 768 entries, 0 to 767\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Pregnancies 768 non-null int64 \n", " 1 Glucose 768 non-null float64\n", " 2 BloodPressure 768 non-null float64\n", " 3 SkinThickness 768 non-null float64\n", " 4 Insulin 768 non-null float64\n", " 5 BMI 768 non-null float64\n", " 6 DiabetesPedigreeFunction 768 non-null float64\n", " 7 Age 768 non-null int64 \n", " 8 Outcome 768 non-null int64 \n", "dtypes: float64(6), int64(3)\n", "memory usage: 54.1 KB\n" ] }, { "data": { "text/plain": [ "(None,\n", " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", " 0 6 148.0 72.0 35.0 125.0 33.6 \n", " 1 1 85.0 66.0 29.0 125.0 26.6 \n", " 2 8 183.0 64.0 29.0 125.0 23.3 \n", " 3 1 89.0 66.0 23.0 94.0 28.1 \n", " 4 0 137.0 40.0 35.0 168.0 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": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_filled.info(),df_filled.head()" ] }, { "cell_type": "code", "execution_count": 22, "id": "9a49aff2", "metadata": {}, "outputs": [], "source": [ "missing_after = df_filled.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 23, "id": "58ecf666", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "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\n" ] } ], "source": [ "#print the missing data after replace null to median value \n", "print(missing_after)" ] }, { "cell_type": "code", "execution_count": 24, "id": "24eceeaa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
06148.072.035.0125.033.60.627501
1185.066.029.0125.026.60.351310
28183.064.029.0125.023.30.672321
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40137.040.035.0168.043.12.288331
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" ], "text/plain": [ " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", "0 6 148.0 72.0 35.0 125.0 33.6 \n", "1 1 85.0 66.0 29.0 125.0 26.6 \n", "2 8 183.0 64.0 29.0 125.0 23.3 \n", "3 1 89.0 66.0 23.0 94.0 28.1 \n", "4 0 137.0 40.0 35.0 168.0 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": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_filled.head()" ] }, { "cell_type": "code", "execution_count": 27, "id": "1527109d", "metadata": {}, "outputs": [], "source": [ "#use train test spliti from sklearn library (i imported one functons from sklearn library ,it' will be more speed )\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 31, "id": "c0b54849", "metadata": {}, "outputs": [], "source": [ "#we dromed outcome col because it will contain our result \n", "X = df_filled.drop('Outcome',axis=1)\n", "#create variable name y that will be contain the prediction\n", "y = df_filled['Outcome']" ] }, { "cell_type": "code", "execution_count": 33, "id": "5bea39e6", "metadata": {}, "outputs": [], "source": [ "#sepirate the data betwen training 80% and testing 20%\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)" ] }, { "cell_type": "code", "execution_count": 35, "id": "45d423e6", "metadata": {}, "outputs": [], "source": [ "#to select model , it's import the libraries\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score" ] }, { "cell_type": "code", "execution_count": 41, "id": "a3d88df2", "metadata": {}, "outputs": [], "source": [ "#define the value that we need to tested \n", "param_grid = {\n", " 'C': [0.01, 0.1, 1, 10, 100],\n", " 'solver': ['liblinear', 'lbfgs'],\n", " 'max_iter': [500, 1000, 2000, 5000] # زدنا التكرارات أكثر\n", "}" ] }, { "cell_type": "code", "execution_count": 42, "id": "56f13ee7", "metadata": {}, "outputs": [], "source": [ "#create the model \n", "log_reg = LogisticRegression()" ] }, { "cell_type": "code", "execution_count": 43, "id": "0922a93d", "metadata": {}, "outputs": [], "source": [ "#prepare grid search\n", "grid_search = GridSearchCV(\n", "estimator=log_reg,\n", " param_grid=param_grid,\n", " cv=5,\n", " scoring='accuracy',\n", " n_jobs=1\n", ")" ] }, { "cell_type": "code", "execution_count": 44, "id": "b8eafe12", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=1,\n", " param_grid={'C': [0.01, 0.1, 1, 10, 100],\n", " 'max_iter': [500, 1000, 2000, 5000],\n", " 'solver': ['liblinear', 'lbfgs']},\n", " scoring='accuracy')" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#train the model\n", "grid_search.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 45, "id": "171fbb9e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best of params wht grid search found\n", "{'C': 1, 'max_iter': 500, 'solver': 'lbfgs'}\n" ] } ], "source": [ "print('best of params wht grid search found')\n", "print(grid_search.best_params_)" ] }, { "cell_type": "code", "execution_count": 47, "id": "c8151fb2", "metadata": {}, "outputs": [], "source": [ "#predict training data \n", "y_pred = grid_search.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 48, "id": "ab0f5e2d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "تقرير التصنيف Classification Report:\n", "\n", " precision recall f1-score support\n", "\n", " 0 0.75 0.81 0.78 100\n", " 1 0.59 0.50 0.54 54\n", "\n", " accuracy 0.70 154\n", " macro avg 0.67 0.66 0.66 154\n", "weighted avg 0.69 0.70 0.70 154\n", "\n", "مصفوفة الالتباس Confusion Matrix:\n", "\n", "[[81 19]\n", " [27 27]]\n", "الدقة (Accuracy): 0.7012987012987013\n" ] } ], "source": [ "print(\"\\nتقرير التصنيف Classification Report:\\n\")\n", "print(classification_report(y_test, y_pred))\n", "\n", "print(\"مصفوفة الالتباس Confusion Matrix:\\n\")\n", "print(confusion_matrix(y_test, y_pred))\n", "\n", "print(\"الدقة (Accuracy):\", accuracy_score(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 50, "id": "57ff2563", "metadata": {}, "outputs": [], "source": [ "#import libraries\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score" ] }, { "cell_type": "code", "execution_count": 53, "id": "92812b2b", "metadata": {}, "outputs": [], "source": [ "#prepare param_grade for each model (Random Forest)\n", "param_grid_rf = {\n", " 'n_estimators': [50, 100, 200], \n", " 'max_depth': [None, 10, 20, 30], \n", " 'min_samples_split': [2, 5, 10] \n", "}" ] }, { "cell_type": "code", "execution_count": 56, "id": "122416f1", "metadata": {}, "outputs": [], "source": [ "#SVM\n", "param_grid_svm = {\n", " 'C': [0.1, 1, 10],\n", " 'kernel': ['linear', 'rbf'], \n", " 'gamma': ['scale', 'auto'] \n", "}\n" ] }, { "cell_type": "code", "execution_count": 57, "id": "41bc2fa0", "metadata": {}, "outputs": [], "source": [ "#prepare grid search for each model\n", "rf_model = RandomForestClassifier(random_state=42)\n", "\n", "grid_search_rf = GridSearchCV(\n", " estimator=rf_model,\n", " param_grid=param_grid_rf,\n", " cv=5,\n", " scoring='accuracy',\n", " n_jobs=-1\n", ")" ] }, { "cell_type": "code", "execution_count": 58, "id": "850fd3f4", "metadata": {}, "outputs": [], "source": [ "#SVM Grid Search\n", "svm_model = SVC()\n", "\n", "grid_search_svm = GridSearchCV(\n", " estimator=svm_model,\n", " param_grid=param_grid_svm,\n", " cv=5,\n", " scoring='accuracy',\n", " n_jobs=-1\n", ")" ] }, { "cell_type": "code", "execution_count": 60, "id": "7c9f7692", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(cv=5, estimator=SVC(), n_jobs=-1,\n", " param_grid={'C': [0.1, 1, 10], 'gamma': ['scale', 'auto'],\n", " 'kernel': ['linear', 'rbf']},\n", " scoring='accuracy')" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#train the model \n", "grid_search_rf.fit(X_train, y_train)\n", "grid_search_svm.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 61, "id": "ddbe0303", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🔵 Random Forest Best Params: {'max_depth': None, 'min_samples_split': 2, 'n_estimators': 100}\n", " precision recall f1-score support\n", "\n", " 0 0.80 0.88 0.84 100\n", " 1 0.73 0.59 0.65 54\n", "\n", " accuracy 0.78 154\n", " macro avg 0.76 0.74 0.75 154\n", "weighted avg 0.77 0.78 0.77 154\n", "\n", "Confusion Matrix:\n", " [[88 12]\n", " [22 32]]\n", "Accuracy: 0.7792207792207793\n", "\n", "==================================================\n", "\n", "🟠 SVM Best Params: {'C': 10, 'gamma': 'scale', 'kernel': 'rbf'}\n", " precision recall f1-score support\n", "\n", " 0 0.75 0.88 0.81 100\n", " 1 0.68 0.46 0.55 54\n", "\n", " accuracy 0.73 154\n", " macro avg 0.71 0.67 0.68 154\n", "weighted avg 0.73 0.73 0.72 154\n", "\n", "Confusion Matrix:\n", " [[88 12]\n", " [29 25]]\n", "Accuracy: 0.7337662337662337\n" ] } ], "source": [ "#evaluate the mode\n", "print(\"🔵 Random Forest Best Params:\", grid_search_rf.best_params_)\n", "y_pred_rf = grid_search_rf.predict(X_test)\n", "print(classification_report(y_test, y_pred_rf))\n", "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred_rf))\n", "print(\"Accuracy:\", accuracy_score(y_test, y_pred_rf))\n", "\n", "print(\"\\n\" + \"=\"*50 + \"\\n\")\n", "\n", "# SVM\n", "print(\"🟠 SVM Best Params:\", grid_search_svm.best_params_)\n", "y_pred_svm = grid_search_svm.predict(X_test)\n", "print(classification_report(y_test, y_pred_svm))\n", "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred_svm))\n", "print(\"Accuracy:\", accuracy_score(y_test, y_pred_svm))" ] }, { "cell_type": "code", "execution_count": 62, "id": "987e9657", "metadata": {}, "outputs": [], "source": [ "#4(Use two feature selection methods to identify the most important features from the datasets- first one)\n", "from sklearn.feature_selection import SelectKBest, chi2" ] }, { "cell_type": "code", "execution_count": 63, "id": "aaf6c5b3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SelectKBest(k=5, score_func=)" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#selected best of 5 featurs\n", "selector = SelectKBest(score_func=chi2, k=5)\n", "selector.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 64, "id": "4638a9ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🔵 Top Features selected by SelectKBest (chi2):\n", "Index(['Glucose', 'SkinThickness', 'Insulin', 'BMI', 'Age'], dtype='object')\n" ] } ], "source": [ "#apply the selected features\n", "selected_features_kbest = X_train.columns[selector.get_support()]\n", "print(\"🔵 Top Features selected by SelectKBest (chi2):\")\n", "print(selected_features_kbest)" ] }, { "cell_type": "code", "execution_count": 66, "id": "65fba73e", "metadata": {}, "outputs": [], "source": [ "#seconde one \n", "#to evaluate the inportant model use Random forest\n", "rf = RandomForestClassifier(random_state=42)\n", "rf.fit(X_train, y_train)\n", "importances = rf.feature_importances_\n" ] }, { "cell_type": "code", "execution_count": 67, "id": "378b9013", "metadata": {}, "outputs": [], "source": [ "#arrange the important featurs\n", "feature_importances_rf = pd.Series(importances, index=X_train.columns)\n", "feature_importances_rf = feature_importances_rf.sort_values(ascending=False)" ] }, { "cell_type": "code", "execution_count": 68, "id": "e3bf94d2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "🟠 Top Features selected by Random Forest Importance:\n", "Glucose 0.274086\n", "BMI 0.161903\n", "DiabetesPedigreeFunction 0.125020\n", "Age 0.112985\n", "Insulin 0.091224\n", "dtype: float64\n" ] } ], "source": [ "print(\"\\n🟠 Top Features selected by Random Forest Importance:\")\n", "print(feature_importances_rf.head(5)) # نعرض أهم 5 خصائص" ] }, { "cell_type": "code", "execution_count": 69, "id": "05c34911", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#addetional - drawing chart for important features \n", "import matplotlib.pyplot as plt\n", "\n", "feature_importances_rf.head(10).plot(kind='barh')\n", "plt.title('Top 10 Important Features (Random Forest)')\n", "plt.xlabel('Importance Score')\n", "plt.gca().invert_yaxis()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 71, "id": "d31e3b90", "metadata": {}, "outputs": [], "source": [ "#5.Apply machine learning models to the selected features and evaluate the models\n", "selected_features = ['Glucose', 'BMI', 'Age', 'BloodPressure', 'DiabetesPedigreeFunction']" ] }, { "cell_type": "code", "execution_count": 72, "id": "88c54329", "metadata": {}, "outputs": [], "source": [ "#select the important featurs\n", "X_train_selected = X_train[selected_features]\n", "X_test_selected = X_test[selected_features]" ] }, { "cell_type": "code", "execution_count": 73, "id": "11c22f7c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(max_iter=1000, random_state=42)" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#applied Logistic Regression & Random forest\n", "from sklearn.linear_model import LogisticRegression\n", "\n", "model = LogisticRegression(max_iter=1000, random_state=42)\n", "model.fit(X_train_selected, y_train)" ] }, { "cell_type": "code", "execution_count": 74, "id": "54c72f9a", "metadata": {}, "outputs": [], "source": [ "#predict \n", "y_pred_selected = model.predict(X_test_selected)" ] }, { "cell_type": "code", "execution_count": 75, "id": "61bfbb34", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 0 1 0 0 1 0 1 1 0 1 0 0 0 0 0 0 1 1 0 0\n", " 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 1 0 1 0 1 0 1\n", " 1 0 0 0 0 0 0 1 0 1 0 0 1 0 1 1 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1\n", " 0 0 0 0 1 0]\n" ] } ], "source": [ "print(y_pred_selected)" ] }, { "cell_type": "code", "execution_count": 76, "id": "c1f261fe", "metadata": {}, "outputs": [], "source": [ "#evaluate the result on the seleceted featurs\n", "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n" ] }, { "cell_type": "code", "execution_count": 77, "id": "d9bb5567", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🔵 تقييم النموذج باستخدام الخصائص المختارة:\n", " precision recall f1-score support\n", "\n", " 0 0.76 0.82 0.79 100\n", " 1 0.61 0.52 0.56 54\n", "\n", " accuracy 0.71 154\n", " macro avg 0.68 0.67 0.67 154\n", "weighted avg 0.71 0.71 0.71 154\n", "\n", "Confusion Matrix:\n", " [[82 18]\n", " [26 28]]\n", "Accuracy: 0.7142857142857143\n" ] } ], "source": [ "print(\"🔵 تقييم النموذج باستخدام الخصائص المختارة:\")\n", "print(classification_report(y_test, y_pred_selected))\n", "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred_selected))\n", "print(\"Accuracy:\", accuracy_score(y_test, y_pred_selected))" ] }, { "cell_type": "code", "execution_count": null, "id": "de083f68", "metadata": {}, "outputs": [], "source": [] } ], "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": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 5 }