How shanmukharam27/house-price-prediction works
This is an educational house-price regression project whose documented workflow runs sequentially in a Jupyter notebook: load the Kaggle data, explore and transform features, train and compare regressors, then predict prices and prepare a Kaggle submission. The notebook is the only apparent runtime entry point; the tree contains saved model artifacts and output directories. The workflow links and capabilities below come from the README; no notebook source was sampled, so implementation details and artifact usage cannot be verified.
Main entry points: house_price_project_separated (1).ipynb Model training · models Saved models · outputs Project visualizations
Components 17
Data preparation
- Project notebook
notebooks/house_price_project_separated (1).ipynb - Data loading
notebooks/house_price_project_separated (1).ipynb - Exploratory analysis
notebooks/house_price_project_separated (1).ipynb - Missing values
notebooks/house_price_project_separated (1).ipynb - Outlier detection
notebooks/house_price_project_separated (1).ipynb - Feature engineering
notebooks/house_price_project_separated (1).ipynb - Feature transforms
notebooks/house_price_project_separated (1).ipynb
Modeling
- Model training
notebooks/house_price_project_separated (1).ipynb - Cross-validation
notebooks/house_price_project_separated (1).ipynb - Model comparison
notebooks/house_price_project_separated (1).ipynb - Feature importance
notebooks/house_price_project_separated (1).ipynb - Saved models
models
Results
- Final prediction
notebooks/house_price_project_separated (1).ipynb - Project visualizations
outputs - Kaggle submission
Other components
- Kaggle house data
- Kaggle
Connections 16
- Project notebook → Data loading: runs
- Kaggle house data → Data loading: loads
- Data loading → Exploratory analysis: feeds
- Exploratory analysis → Missing values: informs
- Missing values → Outlier detection: feeds
- Outlier detection → Feature engineering: feeds
- Feature engineering → Feature transforms: feeds
- Feature transforms → Model training: feeds
- Model training → Cross-validation: evaluates
- Cross-validation → Model comparison: compares
- Model comparison → Feature importance: analyzes
- Model comparison → Final prediction: selects
- Final prediction → Kaggle submission: formats
- Kaggle submission → Kaggle: submits to
- Project notebook → Project visualizations: generates
- Model training → Saved models: produces