Here’s a clean, professional README.md for your Heart Disease Detection project 👇

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🩺 Heart Disease Detection using Machine Learning

📘 Overview

This project predicts whether a patient is likely to have heart disease based on their medical attributes such as age, blood pressure, cholesterol level, and more. It uses Random Forest Classifier — a powerful and interpretable machine learning model — to perform binary classification.

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🧠 Objective

To build an ML model that accurately classifies patients as:

• 0 → Normal (No Heart Disease)
• 1 → Heart Disease

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🧾 Dataset

The dataset (dataset.csv) contains patient data with the following attributes:

Feature	Description	Type
age	Age in years	Numeric
sex	0 = Female, 1 = Male	Binary
chest_pain_type	1–4 (Types of chest pain)	Nominal
resting_bp_s	Resting blood pressure (mm Hg)	Numeric
cholesterol	Serum cholesterol (mg/dL)	Numeric
fasting_blood_sugar	1 if >120 mg/dL else 0	Binary
resting_ecg	ECG results (0–2)	Nominal
max_heart_rate	Maximum heart rate achieved	Numeric
exercise_angina	1 = Yes, 0 = No	Binary
oldpeak	ST depression induced by exercise	Numeric
st_slope	1 = Up, 2 = Flat, 3 = Down	Nominal
target	0 = Normal, 1 = Heart Disease	Binary

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⚙️ Steps Performed

1. Data Preprocessing

   • Cleaned column names
   • Handled missing values using median/mode imputation
   • Split dataset into training and testing (80–20)
   • Scaled numeric features

2. Model Building

   • Trained a Random Forest Classifier
   • Used balanced class weights to handle class imbalance

3. Evaluation Metrics

   • Accuracy
   • Precision
   • Recall
   • F1 Score
   • ROC-AUC

4. Visualization

   • Confusion Matrix
   • ROC Curve

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🧩 Technologies Used

• Python 3.9+
• pandas, numpy
• scikit-learn
• matplotlib
• joblib

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🚀 How to Run

1. Clone this repository

git clone https://github.com/<your-username>/heart-disease-detector.git
cd heart-disease-detector

2. Install dependencies

pip install -r requirements.txt

3. Run the model

python detect_heart_disease.py

4. Output files

• best_heart_model.joblib — Trained Random Forest model
• confusion_matrix and roc_curve plots — Saved/displayed after training

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📊 Example Results

Metric	Score
Accuracy	0.92
Precision	0.93
Recall	0.93
F1 Score	0.93
ROC-AUC	0.97

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💾 Model Saving

The trained model is saved using joblib:

joblib.dump(pipe, "best_heart_model.joblib")

You can later load it for predictions:

model = joblib.load("best_heart_model.joblib")
pred = model.predict(new_data)

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🧬 Future Improvements

• Integrate Flask API for real-time prediction
• Add feature importance analysis (SHAP or permutation importance)
• Deploy using Streamlit or FastAPI

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👨‍💻 Author

GitHub: https://github.com/Linux56ax