❤️ Heart Stroke Risk Prediction

This project is a machine learning-powered web app that predicts the likelihood of heart disease based on user-provided health parameters.
It uses a Logistic Regression model trained on cardiovascular data, selected after evaluating multiple supervised classification algorithms.

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📋 Features

• User-friendly Streamlit web interface
• Accepts health parameters like age, cholesterol, blood pressure, heart rate, etc.
• Uses the same preprocessing pipeline from training (scaler + expected columns)
• Real-time prediction with a clear High Risk / Low Risk output message

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📊 Model Selection

We tested the following supervised classification algorithms:

1. Logistic Regression ✅ (best performer)
2. K-Nearest Neighbors (KNN)
3. Support Vector Machine (SVM)
4. Decision Tree
5. Naive Bayes

Logistic Regression was chosen based on the best performance in terms of accuracy and f1_score.

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📦 Installation & Running Locally

1️⃣ Clone this repository

git clone https://github.com/Ariz253/HeartWise.git
cd HeartWise

2️⃣ Create a virtual environment (recommended)

python -m venv venv
# On Mac/Linux
source venv/bin/activate
# On Windows
venv\Scripts\activate

3️⃣ Install dependencies

pip install -r requirements.txt

4️⃣ Ensure model files are present

The following files should be in the project root:

• heart_disease_model.pkl → trained Logistic Regression model
• heart_disease_scaler.pkl → scaler object used in preprocessing
• heart_disease_columns.pkl → expected column names for input data

5️⃣ Run the Streamlit app

streamlit run app.py