"This project is a Machine Learning API built using FastAPI and Scikit-learn. I trained a Random Forest Regression model on the California Housing dataset to predict house prices. After preprocessing the data and training the model, I saved it using Joblib. Then I developed REST APIs with FastAPI that accept housing features as JSON input and return predicted prices in real time. I also used Pydantic for input validation and Swagger UI for API testing. This project helped me understand the complete machine learning workflow—from data preprocessing and model training to deployment and serving predictions through production-ready APIs."