AI Engineering
medium
~30 hours
Real Estate Price Predictor with Full ML Pipeline
End-to-end ML project: data collection, feature engineering, model selection (linear regression through gradient boosting), hyperparameter tuning, and deployment with a REST API.
Skills Demonstrated
Feature engineering and selection
Model comparison (linear, tree-based, ensemble)
Cross-validation and hyperparameter tuning
Model deployment with FastAPI
Implementation Steps
- Collect and clean real estate dataset
- Exploratory data analysis with Pandas + Matplotlib
- Feature engineering: encode categoricals, scale numerics
- Train and compare: Linear Regression, Random Forest, XGBoost
- Hyperparameter tuning with GridSearchCV/Optuna
- Build FastAPI prediction API with input validation
- Create interactive Gradio/Streamlit demo
Interview Relevance
Why this project matters for interviews
Classic ML interview project. Shows you understand the full pipeline, not just model.fit(). Feature engineering and model comparison are the most tested skills in ML engineer interviews.