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Heart-Disease-Dataset

A machine learning-based system that analyzes health parameters to predict the risk of heart disease.

❤️ Heart Disease Risk Prediction Project

📌 Overview

This project focuses on analyzing and predicting heart disease risk using healthcare data. It covers the full data pipeline from raw dataset → cleaned dataset → analysis → machine learning model.

🧾 Dataset Description

🔹 1. Original Dataset

  • File: Data_Heart Problem_risk.csv

  • Contains raw medical and lifestyle data

  • May include:

    • Missing values
    • Inconsistent entries
    • Noise

🔹 2. Cleaned Dataset

  • File: Data_Heart_Problem_Risk_Cleaned.csv

  • Data preprocessing steps include:

    • Handling missing values
    • Removing duplicates
    • Encoding categorical variables
    • Feature scaling (if applied)

📊 Data Analysis

🔹 3. Analysis File

  • File: Heart_Problem_Analysis.py

  • Includes:

    • Exploratory Data Analysis (EDA)
    • Data visualization
    • Statistical insights
    • Identification of key risk factors

🤖 Machine Learning

🔹 4. ML Notebook

  • File: Heart_Problem-ML.ipynb

  • Contains:

    • Data loading
    • Feature selection
    • Model training
    • Model evaluation

📈 Algorithms Used

  • Logistic Regression
  • Decision Tree
  • Random Forest
  • Support Vector Machine (SVM)

🎯 Objectives

  • Analyze heart disease risk factors
  • Perform data visualization and insights
  • Build predictive machine learning models
  • Evaluate model performance and accuracy

🚀 How to Run the Project

1. Install Dependencies

pip install pandas numpy matplotlib seaborn scikit-learn jupyter

2. Run Analysis Script

python Heart_Problem_Analysis.py

3. Open ML Notebook

jupyter notebook Heart_Problem-ML.ipynb

📊 Workflow

  1. Load raw dataset
  2. Clean and preprocess data
  3. Perform data analysis
  4. Train machine learning models
  5. Evaluate results

⚠️ Disclaimer

  • This project is for educational purposes only
  • Not intended for medical diagnosis
  • Consult professionals for real-world medical decisions

📌 Future Enhancements

  • Hyperparameter tuning
  • Model comparison dashboard
  • Deployment using Flask/Streamlit
  • Integration with real-time data

👩‍💻 Author

Archita J Laxman

📄 License

This project is licensed under the MIT License.