Build & Deploy Heart Disease Predictor Using Random Forest | FastAPI | IIT KANPUR

🩺 Heart Failure Prediction using Random Forest & FastAPI | ML Model Deployment Project

📁 My Kaggle Profile: https://www.kaggle.com/codingloading
💻 My GitHub Repository: https://github.com/DataScientist00
📦 Dataset Source:
Heart Failure Clinical Records Dataset (UCI / Kaggle)
https://www.kaggle.com/datasets/andrewmvd/heart-failure-clinical-data

💻 Explore the Full Project Code:
https://www.kaggle.com/code/codingloading/heart-failure-prediction-with-8-models-ann

📖 In this project, I built a machine learning pipeline to predict the risk of heart failure using Random Forest. The model is trained on clinical health records and deployed as a RESTful API using FastAPI for real-time inference.

🎯 The goal of this project is to demonstrate an end-to-end ML lifecycle—from training to model serving with a lightweight API.

🔍 What’s Covered?
✅ Data preprocessing & feature engineering
✅ Training a Random Forest classifier
✅ Model evaluation with metrics like AUC & accuracy
✅ Saving the model with Joblib
✅ Creating a FastAPI app to serve predictions
✅ Making POST requests to test the deployed API

🚀 Perfect for Data Scientists, ML Engineers, and Developers looking to learn real-world ML deployment with Python, scikit-learn, and FastAPI.

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