Data-Sets-Analysis

🩺 Cardiovascular Risk Prediction System

An AI-powered Streamlit web application that predicts the risk of cardiovascular disease using machine learning models like XGBoost and TensorFlow.
The app provides personalized predictions, data visualizations, health advice, and feature importance analysis — all in an intuitive interface.

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🚀 Features

💡 Core ML Features

• 🔍 AI Prediction Engine (XGBoost + TensorFlow) — predicts cardiovascular risk accurately.
• 🧠 Feature Importance Chart — highlights the top predictors influencing your health risk.
• 💾 Save Predictions Automatically — every user prediction is stored in predictions_log.csv.
• 🩸 Health Advice Generator — dynamic lifestyle recommendations based on your results.
• 📊 Risk Summary Dashboard — bar and pie charts showing high vs low risk ratios.

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📊 Dashboard Visualizations

• Feature Importance Chart: Visualizes which factors (age, cholesterol, BP, etc.) affect risk most.
• Risk Distribution Chart: Displays percentage of High vs. Low risk predictions.
• Prediction History Table: Lists all saved predictions with timestamps and patient details.

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🧠 Machine Learning Model

The model (cardio_risk_model.pkl) is trained using:

• Algorithm: XGBoost Classifier (binary:logistic)
• Data Scaling: StandardScaler for numerical features
• Dataset Size: 3000 synthetic patient records
• Key Features:
  • Age, Height, Weight, Gender
  • Blood Pressure (Systolic, Diastolic)
  • Cholesterol & Glucose Levels
  • Lifestyle (Smoking, Alcohol, Activity)
  • Newly Added Features:
    • Body Mass Index (BMI)
    • Family History
    • Diet Quality
    • Sleep Hours
    • Stress Level
    • Resting Heart Rate

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🖥️ How to Run the Project

1️⃣ Clone this Repository

https://github.com/Dhilshan-codebox/DataSets-Analysis.git
cd DataSets-Analysis