❤️ Cardio risk Predictor – Machine Learning Web Application

A complete end-to-end Machine Learning project that predicts the likelihood of heart disease based on patient health parameters.
This project covers the entire ML lifecycle — from data preprocessing and feature engineering to model training, evaluation, and deployment using Flask.

Designed as a portfolio-grade data science & ML application with clean code structure and real-world workflow.

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🚀 Project Highlights

• 🧠 Cardio-Risk-Predictor using supervised machine learning
• 🧹 Missing Value Handling & Data Cleaning
• ⚖️ Class Imbalance Handling
• 🔄 Feature Transformation & Outlier Treatment
• 📊 Model Training, Evaluation & Selection
• 💾 Model & Scaler Serialization (Pickle)
• 🌐 Flask Web Application for real-time predictions
• 🎨 Simple & clean UI for user interaction

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🧠 Machine Learning Pipeline

The project follows a structured and industry-standard ML workflow:

1. Data Ingestion

   • Dataset loaded from heart.csv

2. Data Preprocessing

   • Missing value handling
   • Data type correction
   • Feature scaling using StandardScaler

3. Feature Engineering

   • Variable transformations
   • Outlier detection & handling

4. Class Imbalance Handling

   • Balancing techniques applied to improve model fairness

5. Model Training

   • Multiple ML models trained and evaluated
   • Best-performing model selected

6. Model Persistence

   • Final model saved as best_model.pkl
   • Scaler saved as scaler.pkl

7. Model Deployment

   • Flask-based web application for predictions

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🛠️ Tech Stack

Programming & Frameworks

• Python
• Flask

Machine Learning & Data Science

• NumPy
• Pandas
• Scikit-learn

Visualization / UI

• HTML
• CSS

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📂 Project Structure

Cardio-Risk-Predictor/
│
├── app.py                         # Flask application
├── main.py                        # Project execution flow
│
├── heart.csv                      # Dataset
│
├── missing_val_handle.py          # Missing value handling
├── variable_transformation_outlierhandle.py
│                                  # Feature transformation & outlier treatment
├── data_balance.py                # Class imbalance handling
│
├── model_training.py              # Model training & evaluation
├── log_code.py                    # Logging utilities
│
├── best_model.pkl                 # Trained ML model
├── scaler.pkl                     # Feature scaler
│
├── templates/
│   ├── index.html                 # Input form
│   └── result.html                # Prediction result page
│
├── static/
│   └── style.css                  # UI styling
│
└── README.md

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▶️ How to Run the Project Locally

1️⃣ Clone the Repository

git clone https://github.com/amareshtoxico/Cardio-Risk-Predictor.git
cd heart-disease-prediction-ml

2️⃣ Create & Activate Virtual Environment

python -m venv venv
venv\Scripts\activate   # Windows

3️⃣ Install Dependencies

pip install flask numpy pandas scikit-learn

4️⃣ Run the Flask App

python app.py

5️⃣ Open in Browser

http://127.0.0.1:5000

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🧪 Input Features

The model predicts heart disease based on clinical parameters such as:

• Age
• Sex
• Chest pain type
• Resting blood pressure
• Cholesterol level
• Fasting blood sugar
• Maximum heart rate achieved
• Exercise-induced angina
• ST depression and slope

(Exact inputs depend on dataset features)

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🎯 Use Cases

• Healthcare risk prediction systems
• Medical decision support tools
• Machine Learning portfolio project
• Data science academic projects
• Flask + ML deployment reference

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🔮 Future Enhancements

• Model explainability (SHAP / feature importance)
• Improved UI/UX design
• REST API support
• Cloud deployment (Render / Railway)
• User authentication & history tracking

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👨‍💻 Author

Amaresh Virupakshi Machine Learning & Python Developer

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✅ Why This README Is Strong

✔ Shows real ML depth (not toy project)
✔ Clearly explains preprocessing & feature engineering
✔ Recruiter-friendly structure
✔ Demonstrates end-to-end ownership
✔ Matches actual files in your project