❤️ Heart Disease Classification

Welcome to the Heart Disease Classification Dataset repository! This dataset contains medical attributes that can help in predicting the presence of heart disease. It is useful for researchers, data scientists, and healthcare professionals working on cardiovascular disease prediction models.

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📂 Dataset Overview

• Filename: heart.csv
• Format: CSV (Comma-Separated Values)
• Size: Contains multiple records of patient data
• Purpose: Used for machine learning models to predict heart disease

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📊 Features & Column Descriptions

Column Name	Description
age	Age of the patient
sex	Gender (1 = Male, 0 = Female)
cp	Chest Pain Type (Categorical: 0-3)
trestbps	Resting Blood Pressure (mm Hg)
chol	Serum Cholesterol (mg/dL)
fbs	Fasting Blood Sugar (> 120 mg/dL, 1 = True, 0 = False)
restecg	Resting Electrocardiographic Results (0-2)
thalach	Maximum Heart Rate Achieved
exang	Exercise-Induced Angina (1 = Yes, 0 = No)
oldpeak	ST Depression Induced by Exercise
slope	Slope of Peak Exercise ST Segment (0-2)
ca	Number of Major Vessels (0-3)
thal	Thalassemia (0-3)
target	Presence of Heart Disease (1 = Yes, 0 = No)

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🛠️ Usage Instructions

1. Load the dataset in Python:

   import pandas as pd
   df = pd.read_csv("heart.csv")
   print(df.head())

2. Perform Exploratory Data Analysis (EDA):

   import seaborn as sns
   import matplotlib.pyplot as plt
   
   sns.pairplot(df, hue='target')
   plt.show()

3. Train a Machine Learning Model:

   from sklearn.model_selection import train_test_split
   from sklearn.ensemble import RandomForestClassifier
   from sklearn.metrics import accuracy_score
   
   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)
   model = RandomForestClassifier()
   model.fit(X_train, y_train)
   predictions = model.predict(X_test)
   
   print("Accuracy:", accuracy_score(y_test, predictions))

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🎯 Applications

• Heart Disease Prediction Models 🏥
• Medical Research & Data Analysis 🔬
• Machine Learning & AI Development 🤖
• Academic Projects & Tutorials 📚

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📜 License

This dataset is free to use for research and educational purposes. However, always cite the source if using it in publications.

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👥 Contributors

• Surya K ([email protected])
• Open to Contributions! 🚀

For any questions or contributions, feel free to open an issue or submit a pull request. Enjoy analyzing! 🎉