House Price Prediction

## Project Objective

Develop a complete regression system capable of estimating residential property prices from structured housing features.

The project demonstrates the full Machine Learning lifecycle, including preprocessing, feature engineering, model training, evaluation, explainability, and deployment.

## Dataset

The application uses the Ames Housing dataset, which contains detailed information about residential properties and their final sale prices.

The dataset includes numerical and categorical variables describing property size, quality, location, condition, and construction characteristics.

## Data Preprocessing

• ✓Inspected missing values and inconsistent records
• ✓Handled numerical and categorical features
• ✓Applied feature transformations
• ✓Prepared training and testing datasets
• ✓Created reusable preprocessing logic
• ✓Saved trained artifacts for deployment

## Feature Engineering

• ✓Selected relevant property characteristics
• ✓Prepared categorical variables for model training
• ✓Handled skewed numerical variables
• ✓Improved feature consistency
• ✓Reduced unnecessary input noise
• ✓Prepared the final feature matrix

## Model Development

Multiple regression approaches were explored before selecting XGBoost Regressor as the final model.

The model was optimized using hyperparameter tuning to improve predictive performance and generalization.

## Model Evaluation

• ✓Evaluated regression performance using R² Score
• ✓Measured prediction error using MAE
• ✓Measured error magnitude using RMSE
• ✓Compared predicted prices with actual values
• ✓Reviewed feature importance
• ✓Validated the model on unseen data

## Application Features

• ✓Real-time house price prediction
• ✓Interactive property input form
• ✓Feature-importance visualization
• ✓Explainable prediction outputs
• ✓What-if scenario analysis
• ✓Business-focused insights
• ✓Batch prediction support
• ✓Model monitoring interface

## Deployment

The trained model and preprocessing artifacts were serialized using Joblib.

The final system was deployed as an interactive Streamlit application, allowing users to generate predictions directly from a browser.

## Key Learnings

• ✓Regression model development
• ✓Feature engineering for structured data
• ✓Hyperparameter optimization
• ✓Model evaluation using regression metrics
• ✓Model explainability
• ✓Artifact serialization
• ✓Interactive ML deployment
• ✓End-to-end project organization

## Future Improvements

• ✓Add automated drift detection
• ✓Improve experiment tracking
• ✓Deploy the prediction model through FastAPI
• ✓Containerize the application with Docker
• ✓Add CI/CD for automated deployment
• ✓Integrate model versioning

## Tech Stack

## Completion Date

31 July 2026

## Resources