Heart Disease Prediction – Machine Learning Project

📌 Overview

This project predicts the likelihood of heart disease using machine learning techniques based on patient health data. It includes data preprocessing, exploratory data analysis (EDA), model training, and evaluation using Python and scikit-learn.

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📂 Project Workflow

1. Data Preprocessing

• Handling missing values
• Encoding categorical variables
• Feature scaling
• Removing outliers

2. Exploratory Data Analysis (EDA)

• Distribution analysis
• Correlation heatmaps
• Identifying key factors influencing heart disease

3. Model Building

• Logistic Regression (or your chosen model)
• Train-test split
• Hyperparameter tuning (if applied)

4. Model Evaluation

• Accuracy: 98%
• Precision, Recall, F1-score
• Confusion Matrix

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🧠 Key Highlights

• End-to-end ML workflow
• Clean, readable notebook
• Visual EDA
• Achieved 98% accuracy
• Early disease risk identification

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🛠 Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib / Seaborn
• Jupyter Notebook

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📁 Project Structure

• Heart Disease Prediction
• Data
  • data.csv
• notebook.ipynb

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🧑‍💻 Author

Deepak Kumar
📧 Email : [deepak.kumar8434543@gmail.com]
🔗 LinkedIn : [www.linkedin.com/in/deepak-kumar-acb2002]
🌐 GitHub : [https://github.com/Deepakkumar165]

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📜 License

This project is open-source and available for anyone to use.