{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "b72b4993-2d04-4cde-b036-cf7be49fa69b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 1025 entries, 0 to 1024\n", "Data columns (total 14 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 age 1025 non-null int64 \n", " 1 sex 1025 non-null int64 \n", " 2 cp 1025 non-null int64 \n", " 3 trestbps 1025 non-null int64 \n", " 4 chol 1025 non-null int64 \n", " 5 fbs 1025 non-null int64 \n", " 6 restecg 1025 non-null int64 \n", " 7 thalach 1025 non-null int64 \n", " 8 exang 1025 non-null int64 \n", " 9 oldpeak 1025 non-null float64\n", " 10 slope 1025 non-null int64 \n", " 11 ca 1025 non-null int64 \n", " 12 thal 1025 non-null int64 \n", " 13 target 1025 non-null int64 \n", "dtypes: float64(1), int64(13)\n", "memory usage: 112.2 KB\n" ] }, { "data": { "text/plain": [ "{'head': age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n", " 0 52 1 0 125 212 0 1 168 0 1.0 2 \n", " 1 53 1 0 140 203 1 0 155 1 3.1 0 \n", " 2 70 1 0 145 174 0 1 125 1 2.6 0 \n", " 3 61 1 0 148 203 0 1 161 0 0.0 2 \n", " 4 62 0 0 138 294 1 1 106 0 1.9 1 \n", " \n", " ca thal target \n", " 0 2 3 0 \n", " 1 0 3 0 \n", " 2 0 3 0 \n", " 3 1 3 0 \n", " 4 3 2 0 ,\n", " 'info': None,\n", " 'summary': age sex cp trestbps chol \\\n", " count 1025.000000 1025.000000 1025.000000 1025.000000 1025.00000 \n", " mean 54.434146 0.695610 0.942439 131.611707 246.00000 \n", " std 9.072290 0.460373 1.029641 17.516718 51.59251 \n", " min 29.000000 0.000000 0.000000 94.000000 126.00000 \n", " 25% 48.000000 0.000000 0.000000 120.000000 211.00000 \n", " 50% 56.000000 1.000000 1.000000 130.000000 240.00000 \n", " 75% 61.000000 1.000000 2.000000 140.000000 275.00000 \n", " max 77.000000 1.000000 3.000000 200.000000 564.00000 \n", " \n", " fbs restecg thalach exang oldpeak \\\n", " count 1025.000000 1025.000000 1025.000000 1025.000000 1025.000000 \n", " mean 0.149268 0.529756 149.114146 0.336585 1.071512 \n", " std 0.356527 0.527878 23.005724 0.472772 1.175053 \n", " min 0.000000 0.000000 71.000000 0.000000 0.000000 \n", " 25% 0.000000 0.000000 132.000000 0.000000 0.000000 \n", " 50% 0.000000 1.000000 152.000000 0.000000 0.800000 \n", " 75% 0.000000 1.000000 166.000000 1.000000 1.800000 \n", " max 1.000000 2.000000 202.000000 1.000000 6.200000 \n", " \n", " slope ca thal target \n", " count 1025.000000 1025.000000 1025.000000 1025.000000 \n", " mean 1.385366 0.754146 2.323902 0.513171 \n", " std 0.617755 1.030798 0.620660 0.500070 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 0.000000 2.000000 0.000000 \n", " 50% 1.000000 0.000000 2.000000 1.000000 \n", " 75% 2.000000 1.000000 3.000000 1.000000 \n", " max 2.000000 4.000000 3.000000 1.000000 }" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "# Load the dataset\n", "file_path = 'heart.csv'\n", "data = pd.read_csv(file_path)\n", "\n", "# Display the first few rows and dataset info for inspection\n", "data_info = {\n", " \"head\": data.head(),\n", " \"info\": data.info(),\n", " \"summary\": data.describe(include=\"all\")\n", "}\n", "\n", "data_info\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "4781323a-b5a7-4fbd-b93d-372d8b3f8368", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\HP\\anaconda3\\envs\\alaff\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: 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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters: {'C': 0.01, 'solver': 'liblinear'}\n", "Test Accuracy: 0.775974025974026\n" ] } ], "source": [ "import pandas as pd\n", "from sklearn.feature_selection import RFE\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import train_test_split, GridSearchCV\n", "from sklearn.metrics import accuracy_score\n", "\n", "# Step 1: Load dataset\n", "data = pd.read_csv(\"heart.csv\")\n", "\n", "# Step 2: Define features (X) and target (y)\n", "X = data.drop(columns=['target'])\n", "y = data['target']\n", "\n", "# Step 3: Apply Recursive Feature Elimination (RFE) for feature selection\n", "model = LogisticRegression(max_iter=1000, random_state=42)\n", "rfe = RFE(estimator=model, n_features_to_select=5) # Selecting top 5 features\n", "rfe.fit(X, y)\n", "selected_features = X.columns[rfe.support_] # Get the selected feature names\n", "X_selected = X[selected_features] # Define X_selected with the important features\n", "\n", "# Step 4: Scale the selected features\n", "scaler = StandardScaler()\n", "X_scaled = scaler.fit_transform(X_selected)\n", "\n", "# Step 5: Split the dataset into training and testing sets\n", "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)\n", "\n", "# Step 6: Define hyperparameter grid for Logistic Regression\n", "param_grid = {\n", " 'C': [0.01, 0.1, 1, 10, 100], # Regularization strength\n", " 'solver': ['liblinear', 'lbfgs'], # Optimization solvers\n", "}\n", "\n", "# Step 7: Apply GridSearchCV for hyperparameter tuning\n", "grid_search = GridSearchCV(estimator=LogisticRegression(max_iter=2000, random_state=42),\n", " param_grid=param_grid,\n", " cv=5, # 5-fold cross-validation\n", " scoring='accuracy',\n", " n_jobs=-1)\n", "grid_search.fit(X_train, y_train)\n", "\n", "# Step 8: Get the best parameters and evaluate the model\n", "best_params = grid_search.best_params_\n", "best_model = grid_search.best_estimator_\n", "test_accuracy = accuracy_score(y_test, best_model.predict(X_test))\n", "\n", "print(\"Best Parameters:\", best_params)\n", "print(\"Test Accuracy:\", test_accuracy)\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "a1306a94-8738-4e60-8125-6ab8d33750ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters: {'C': 0.01, 'solver': 'liblinear'}\n", "Test Accuracy: 0.775974025974026\n" ] } ], "source": [ "import pandas as pd\n", "from sklearn.feature_selection import RFE\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import train_test_split, GridSearchCV\n", "from sklearn.metrics import accuracy_score\n", "\n", "# Load dataset\n", "data = pd.read_csv(\"heart.csv\")\n", "\n", "# Define features (X) and target (y)\n", "X = data.drop(columns=['target'])\n", "y = data['target']\n", "\n", "# Apply Recursive Feature Elimination (RFE) for feature selection\n", "model = LogisticRegression(max_iter=2000, random_state=42)\n", "rfe = RFE(estimator=model, n_features_to_select=5) # Selecting top 5 features\n", "rfe.fit(X, y)\n", "selected_features = X.columns[rfe.support_] # Get the selected feature names\n", "X_selected = X[selected_features] # Define X_selected with the important features\n", "\n", "# Scale the selected features\n", "scaler = StandardScaler()\n", "X_scaled = scaler.fit_transform(X_selected)\n", "\n", "# Split the dataset into training and testing sets\n", "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)\n", "\n", "# Define hyperparameter grid for Logistic Regression with increased max_iter and alternative solvers\n", "param_grid = {\n", " 'C': [0.01, 0.1, 1, 10, 100], # Regularization strength\n", " 'solver': ['liblinear', 'lbfgs', 'newton-cg', 'saga'], # Alternative solvers\n", "}\n", "\n", "# Apply GridSearchCV\n", "grid_search = GridSearchCV(estimator=LogisticRegression(max_iter=5000, random_state=42),\n", " param_grid=param_grid,\n", " cv=5, # 5-fold cross-validation\n", " scoring='accuracy',\n", " n_jobs=-1)\n", "grid_search.fit(X_train, y_train)\n", "\n", "# Get the best parameters and evaluate the model\n", "best_params = grid_search.best_params_\n", "best_model = grid_search.best_estimator_\n", "test_accuracy = accuracy_score(y_test, best_model.predict(X_test))\n", "\n", "print(\"Best Parameters:\", best_params)\n", "print(\"Test Accuracy:\", test_accuracy)\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "7ec2bf3e-b407-4239-ad97-de334bafd6fa", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split, GridSearchCV\n", "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import classification_report" ] }, { "cell_type": "code", "execution_count": 11, "id": "3d907b25-7e84-40ca-b2fa-a81aa63c3fa1", "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('heart.csv')" ] }, { "cell_type": "code", "execution_count": 12, "id": "2033cd46-984b-48fd-985f-e88984cfa651", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
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" ], "text/plain": [ " age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n", "0 52 1 0 125 212 0 1 168 0 1.0 2 \n", "1 53 1 0 140 203 1 0 155 1 3.1 0 \n", "2 70 1 0 145 174 0 1 125 1 2.6 0 \n", "3 61 1 0 148 203 0 1 161 0 0.0 2 \n", "4 62 0 0 138 294 1 1 106 0 1.9 1 \n", "\n", " ca thal target \n", "0 2 3 0 \n", "1 0 3 0 \n", "2 0 3 0 \n", "3 1 3 0 \n", "4 3 2 0 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 16, "id": "4cdfd97d-95c6-42f2-9264-819644c8cdd1", "metadata": {}, "outputs": [], "source": [ "label_encoders = {}\n", "for column in data.select_dtypes(include=['object']).columns:\n", " le = LabelEncoder()\n", " data[column] = le.fit_transform(data[column])\n", " label_encoders[column] = le" ] }, { "cell_type": "code", "execution_count": 20, "id": "4b56b98f-e263-46f7-b875-f545030d7ed0", "metadata": {}, "outputs": [ { "ename": "IndentationError", "evalue": "unexpected indent (1260727625.py, line 11)", "output_type": "error", "traceback": [ "\u001b[1;36m Cell \u001b[1;32mIn[20], line 11\u001b[1;36m\u001b[0m\n\u001b[1;33m report = classification_report(y_true, y_pred)\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unexpected indent\n" ] } ], "source": [ "def evaluate_model(model_name, y_true, y_pred):\n", "\n", " # Calculate metrics\n", " accuracy = accuracy_score(y_true, y_pred)\n", " precision = precision_score(y_true, y_pred, average='weighted')\n", " recall = recall_score(y_true, y_pred, average='weighted')\n", " f1 = f1_score(y_true, y_pred, average='weighted')\n", " cm = confusion_matrix(y_true, y_pred)\n", "\n", " # Create a report\n", " report = classification_report(y_true, y_pred)\n", "\n", "\n", " # Output results\n", " metrics = {\n", " 'Model Name': model_name,\n", " 'Accuracy': accuracy,\n", " 'Precision': precision,\n", " 'Recall': recall,\n", " 'F1 Score': f1,\n", "\n", " 'Classification Report': report\n", " }\n", " # Plot Confusion Matrix\n", " plt.figure(figsize=(4, 4))\n", " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False,\n", " xticklabels=np.unique(y_true), yticklabels=np.unique(y_true))\n", " plt.title(f'Confusion Matrix for {model_name}')\n", " plt.xlabel('Predicted Label')\n", " plt.ylabel('True Label')\n", " plt.show()\n", "\n", " return metrics\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "fbc95d96-7d9d-4efe-a46d-1fd1d281b666", "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'panda'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpanda\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'panda'" ] } ], "source": [ "import panda as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "id": "9da953f9-c080-449f-88b6-d8f3cab54def", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "id": "b7584760-6032-4df3-93f3-1cc04ff8c98b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
052101252120116801.02230
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" ], "text/plain": [ " age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n", "0 52 1 0 125 212 0 1 168 0 1.0 2 \n", "1 53 1 0 140 203 1 0 155 1 3.1 0 \n", "2 70 1 0 145 174 0 1 125 1 2.6 0 \n", "3 61 1 0 148 203 0 1 161 0 0.0 2 \n", "4 62 0 0 138 294 1 1 106 0 1.9 1 \n", "\n", " ca thal target \n", "0 2 3 0 \n", "1 0 3 0 \n", "2 0 3 0 \n", "3 1 3 0 \n", "4 3 2 0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load your dataset into a DataFrame\n", "df = pd.read_csv('heart.csv')\n", "# Preview the first 5 rows\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "5fec799c-466e-47ab-9698-1d274b965af1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age int64\n", "sex int64\n", "cp int64\n", "trestbps int64\n", "chol int64\n", "fbs int64\n", "restecg int64\n", "thalach int64\n", "exang int64\n", "oldpeak float64\n", "slope int64\n", "ca int64\n", "thal int64\n", "target int64\n", "dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 5, "id": "df62559a-001c-455f-8978-59fcf177defb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
count1025.0000001025.0000001025.0000001025.0000001025.000001025.0000001025.0000001025.0000001025.0000001025.0000001025.0000001025.0000001025.0000001025.000000
mean54.4341460.6956100.942439131.611707246.000000.1492680.529756149.1141460.3365851.0715121.3853660.7541462.3239020.513171
std9.0722900.4603731.02964117.51671851.592510.3565270.52787823.0057240.4727721.1750530.6177551.0307980.6206600.500070
min29.0000000.0000000.00000094.000000126.000000.0000000.00000071.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%48.0000000.0000000.000000120.000000211.000000.0000000.000000132.0000000.0000000.0000001.0000000.0000002.0000000.000000
50%56.0000001.0000001.000000130.000000240.000000.0000001.000000152.0000000.0000000.8000001.0000000.0000002.0000001.000000
75%61.0000001.0000002.000000140.000000275.000000.0000001.000000166.0000001.0000001.8000002.0000001.0000003.0000001.000000
max77.0000001.0000003.000000200.000000564.000001.0000002.000000202.0000001.0000006.2000002.0000004.0000003.0000001.000000
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" ], "text/plain": [ " age sex cp trestbps chol \\\n", "count 1025.000000 1025.000000 1025.000000 1025.000000 1025.00000 \n", "mean 54.434146 0.695610 0.942439 131.611707 246.00000 \n", "std 9.072290 0.460373 1.029641 17.516718 51.59251 \n", "min 29.000000 0.000000 0.000000 94.000000 126.00000 \n", "25% 48.000000 0.000000 0.000000 120.000000 211.00000 \n", "50% 56.000000 1.000000 1.000000 130.000000 240.00000 \n", "75% 61.000000 1.000000 2.000000 140.000000 275.00000 \n", "max 77.000000 1.000000 3.000000 200.000000 564.00000 \n", "\n", " fbs restecg thalach exang oldpeak \\\n", "count 1025.000000 1025.000000 1025.000000 1025.000000 1025.000000 \n", "mean 0.149268 0.529756 149.114146 0.336585 1.071512 \n", "std 0.356527 0.527878 23.005724 0.472772 1.175053 \n", "min 0.000000 0.000000 71.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 132.000000 0.000000 0.000000 \n", "50% 0.000000 1.000000 152.000000 0.000000 0.800000 \n", "75% 0.000000 1.000000 166.000000 1.000000 1.800000 \n", "max 1.000000 2.000000 202.000000 1.000000 6.200000 \n", "\n", " slope ca thal target \n", "count 1025.000000 1025.000000 1025.000000 1025.000000 \n", "mean 1.385366 0.754146 2.323902 0.513171 \n", "std 0.617755 1.030798 0.620660 0.500070 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 1.000000 0.000000 2.000000 0.000000 \n", "50% 1.000000 0.000000 2.000000 1.000000 \n", "75% 2.000000 1.000000 3.000000 1.000000 \n", "max 2.000000 4.000000 3.000000 1.000000 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 6, "id": "461bdb32-c82b-4d01-bf9e-b8c4c3c0ad0e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 0\n", "sex 0\n", "cp 0\n", "trestbps 0\n", "chol 0\n", "fbs 0\n", "restecg 0\n", "thalach 0\n", "exang 0\n", "oldpeak 0\n", "slope 0\n", "ca 0\n", "thal 0\n", "target 0\n", "dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 7, "id": "52992e25-f4f5-4086-a33e-4044b40169d1", "metadata": {}, "outputs": [], "source": [ "missing_percentage = df.isnull().mean() * 100\n", "threshold = 30" ] }, { "cell_type": "code", "execution_count": 8, "id": "946e5883-95ee-467d-8cb0-cf9220d8b64a", "metadata": {}, "outputs": [], "source": [ "columns_to_drop = missing_percentage[missing_percentage > threshold].index" ] }, { "cell_type": "code", "execution_count": 9, "id": "ed45d97c-099e-48d6-bb3c-c1df1fe5e829", "metadata": {}, "outputs": [], "source": [ "df = df.drop(columns=columns_to_drop)" ] }, { "cell_type": "code", "execution_count": 10, "id": "86df4395-2419-477e-bb22-9d23689d8eb9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns dropped: []\n", "Remaining columns: Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n", " 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n", " dtype='object')\n" ] } ], "source": [ "print(f\"Columns dropped: {list(columns_to_drop)}\")\n", "print(f\"Remaining columns: {df.columns}\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "97054db4-7806-4891-b1eb-722d2ccd80e1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
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" ], "text/plain": [ " age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n", "0 52 1 0 125 212 0 1 168 0 1.0 2 \n", "1 53 1 0 140 203 1 0 155 1 3.1 0 \n", "2 70 1 0 145 174 0 1 125 1 2.6 0 \n", "3 61 1 0 148 203 0 1 161 0 0.0 2 \n", "4 62 0 0 138 294 1 1 106 0 1.9 1 \n", "\n", " ca thal target \n", "0 2 3 0 \n", "1 0 3 0 \n", "2 0 3 0 \n", "3 1 3 0 \n", "4 3 2 0 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "label_encoder = LabelEncoder()\n", "for column in df.select_dtypes(include=['object']).columns:\n", " df[column] = label_encoder.fit_transform(df[column])\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 12, "id": "9b1a7545-5bdd-49e8-a8ea-76d2be5329cb", "metadata": {}, "outputs": [], "source": [ "for column in df.select_dtypes(include=['float64']).columns:\n", " mean_value = df[column].mean() # Get the mode (most frequent value)\n", " df[column] = df[column].fillna(mean_value)" ] }, { "cell_type": "code", "execution_count": 13, "id": "a3084dfc-42c8-47a2-aff0-b360cf1a3b31", "metadata": {}, "outputs": [], "source": [ "for column in df.select_dtypes(include=['object']).columns:\n", " mode_value = df[column].mode()[0] # Get the mode (most frequent value)\n", " df[column] = df[column].fillna(mode_value)" ] }, { "cell_type": "code", "execution_count": 14, "id": "b3c308e2-3695-46f2-8366-64db6f9c32e0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 0\n", "sex 0\n", "cp 0\n", "trestbps 0\n", "chol 0\n", "fbs 0\n", "restecg 0\n", "thalach 0\n", "exang 0\n", "oldpeak 0\n", "slope 0\n", "ca 0\n", "thal 0\n", "target 0\n", "dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 15, "id": "01fa86c1-b3ed-4838-92e0-8d6057635234", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
052101252120116801.02230
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1025 rows × 14 columns

\n", "
" ], "text/plain": [ " age sex cp trestbps chol fbs restecg thalach exang oldpeak \\\n", "0 52 1 0 125 212 0 1 168 0 1.0 \n", "1 53 1 0 140 203 1 0 155 1 3.1 \n", "2 70 1 0 145 174 0 1 125 1 2.6 \n", "3 61 1 0 148 203 0 1 161 0 0.0 \n", "4 62 0 0 138 294 1 1 106 0 1.9 \n", "... ... ... .. ... ... ... ... ... ... ... \n", "1020 59 1 1 140 221 0 1 164 1 0.0 \n", "1021 60 1 0 125 258 0 0 141 1 2.8 \n", "1022 47 1 0 110 275 0 0 118 1 1.0 \n", "1023 50 0 0 110 254 0 0 159 0 0.0 \n", "1024 54 1 0 120 188 0 1 113 0 1.4 \n", "\n", " slope ca thal target \n", "0 2 2 3 0 \n", "1 0 0 3 0 \n", "2 0 0 3 0 \n", "3 2 1 3 0 \n", "4 1 3 2 0 \n", "... ... .. ... ... \n", "1020 2 0 2 1 \n", "1021 1 1 3 0 \n", "1022 1 1 2 0 \n", "1023 2 0 2 1 \n", "1024 1 1 3 0 \n", "\n", "[1025 rows x 14 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 16, "id": "d440f055-d367-4b0c-8465-0c5f62f9757a", "metadata": {}, "outputs": [], "source": [ "def replace_outliers_with_nan(column):\n", " Q1 = column.quantile(0.25)\n", " Q3 = column.quantile(0.75)\n", " IQR = Q3 - Q1\n", " lower_bound = Q1 - 1.5 * IQR\n", " upper_bound = Q3 + 1.5 * IQR\n", " # Replace outliers with NaN\n", " return column.where((column >= lower_bound) & (column <= upper_bound), np.nan)\n", "# Replace outliers with NaN for each feature\n", "for col in df.columns:\n", " df[col] = replace_outliers_with_nan(df[col])" ] }, { "cell_type": "code", "execution_count": 21, "id": "9ac258a9-1cee-499a-a875-653df7802ef5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sum of null values in each column:\n", "age 0\n", "sex 0\n", "cp 0\n", "trestbps 0\n", "chol 0\n", "fbs 0\n", "restecg 0\n", "thalach 0\n", "exang 0\n", "oldpeak 0\n", "slope 0\n", "ca 0\n", "thal 0\n", "target 0\n", "dtype: int64\n" ] } ], "source": [ "import pandas as pd\n", "\n", "# Load the dataset (update the file path as needed)\n", "data = pd.read_csv(\"heart.csv\")\n", "\n", "# Check for null values in each column\n", "null_counts = data.isnull().sum()\n", "print(\"Sum of null values in each column:\")\n", "print(null_counts)" ] }, { "cell_type": "code", "execution_count": 22, "id": "845cf151-b5ba-40ba-8e46-9285be0ac982", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sum of null values in each column:\n", "age 0\n", "sex 0\n", "cp 0\n", "trestbps 0\n", "chol 0\n", "fbs 0\n", "restecg 0\n", "thalach 0\n", "exang 0\n", "oldpeak 0\n", "slope 0\n", "ca 0\n", "thal 0\n", "target 0\n", "dtype: int64\n" ] } ], "source": [ "null_counts = data.isnull().sum()\n", "print(\"Sum of null values in each column:\")\n", "print(null_counts)" ] }, { "cell_type": "code", "execution_count": 25, "id": "31fa08f5-dd4d-4e63-9ca1-abf216145009", "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'matplotlib'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m# Generate the distribution plot for the 'age' column in the dataset\u001b[39;00m\n", "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# Generate the distribution plot for the 'age' column in the dataset\n", "plt.figure(figsize=(8, 6))\n", "sns.histplot(data['age'], bins=15, kde=True, color='blue')\n", "plt.title('Age Distribution')\n", "plt.xlabel('Age')\n", "plt.ylabel('Frequency')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "id": "ca26931b-960c-41ff-a254-12647774aa1c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting matplotlib\n", " Downloading matplotlib-3.10.0-cp312-cp312-win_amd64.whl.metadata (11 kB)\n", "Collecting seaborn\n", " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", "Collecting contourpy>=1.0.1 (from matplotlib)\n", " Downloading contourpy-1.3.1-cp312-cp312-win_amd64.whl.metadata (5.4 kB)\n", "Collecting cycler>=0.10 (from matplotlib)\n", " Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n", "Collecting fonttools>=4.22.0 (from matplotlib)\n", " Downloading fonttools-4.55.3-cp312-cp312-win_amd64.whl.metadata (168 kB)\n", "Collecting kiwisolver>=1.3.1 (from 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# Generate the distribution plot for the 'age' column in the dataset\n", "plt.figure(figsize=(8, 6))\n", "sns.histplot(data['age'], bins=15, kde=True, color='blue')\n", "plt.title('Age Distribution')\n", "plt.xlabel('Age')\n", "plt.ylabel('Frequency')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "1fe97be8-c0fc-44ef-b0a7-5d4e2086bfa8", "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": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }