NDV_Code_By_NandiniM_HeartDiseasePrediction

NDV_Code_BY_NandiniM_HeartDiseasePrediction/HeartDiseasePrediction.py

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+import pandas as pd
+
+# Load Kaggle heart dataset
+df = pd.read_csv("heart.csv")
+print(df.head())
+
+
+# Initial Exploration and Cleaning
+print(df.info())
+print(df.describe())
+print(df.isnull().sum())
+
+
+# Visualize Relationships
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Correlation heatmap
+plt.figure(figsize=(12,8))
+sns.heatmap(df.corr(), annot=True, cmap="coolwarm")
+plt.title("Feature Correlation Heatmap")
+plt.show()
+
+
+
+# Distribution of target classes
+sns.countplot(data=df, x='target')
+plt.title("Target Class Distribution")
+plt.show()
+
+
+
+
+# Age vs. Heart Disease
+sns.boxplot(x='target', y='age', data=df)
+plt.title("Age vs. Heart Disease")
+plt.show()
+
+# Preprocessing
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+
+X = df.drop("target", axis=1)
+y = df["target"]
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+
+# Model Training + Tuning
+
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import GridSearchCV
+
+param_grid = {
+    'n_estimators': [100, 150],
+    'max_depth': [4, 6, 8],
+    'min_samples_split': [2, 4]}
+
+rf = RandomForestClassifier(random_state=42)
+grid = GridSearchCV(rf, param_grid, cv=5)
+grid.fit(X_train, y_train)
+
+print("Best Parameters:", grid.best_params_)
+
+
+# Evaluate the Model
+from sklearn.metrics import classification_report, confusion_matrix
+y_pred = grid.predict(X_test)
+print(confusion_matrix(y_test, y_pred))
+print(classification_report(y_test, y_pred))