How ashly-06/heavy-equipment-selling-price-prediction works
This repository is a notebook-based regression project for predicting heavy-equipment sale prices. Its documented workflow starts with the Kaggle training data, explores and prepares it, engineers features, trains and evaluates three tree-based models, combines predictions, and generates a submission. The README describes these stages, but the notebook contents were not excerpted, so the graph reflects documented relationships rather than verified calls or implementation details.
Main entry points: heavy-equipment-selling-price-prediction_py.ipynb Feature engineering
Components 13
Data preparation
- Exploratory analysis
heavy-equipment-selling-price-prediction_py.ipynb - Preprocessing
heavy-equipment-selling-price-prediction_py.ipynb - Feature engineering
heavy-equipment-selling-price-prediction_py.ipynb
Modeling and evaluation
- CatBoost model
heavy-equipment-selling-price-prediction_py.ipynb - LightGBM model
heavy-equipment-selling-price-prediction_py.ipynb - XGBoost model
heavy-equipment-selling-price-prediction_py.ipynb - CatBoost tuning
heavy-equipment-selling-price-prediction_py.ipynb - Model evaluation
heavy-equipment-selling-price-prediction_py.ipynb
Prediction and results
- Weighted ensemble
heavy-equipment-selling-price-prediction_py.ipynb - Submission generation
heavy-equipment-selling-price-prediction_py.ipynb
Other components
- Analyst
- Kaggle data
- Kaggle leaderboard
Connections 16
- Analyst → Exploratory analysis: explores data
README.md - Kaggle data → Exploratory analysis: provides records
README.md - Exploratory analysis → Preprocessing: informs preparation
README.md - Preprocessing → Feature engineering: prepares inputs
README.md - Feature engineering → CatBoost model: trains model
README.md - Feature engineering → LightGBM model: trains model
README.md - Feature engineering → XGBoost model: trains model
README.md - CatBoost model → CatBoost tuning: tunes hyperparameters
README.md - CatBoost model → Model evaluation: evaluates with RMSLE
README.md - LightGBM model → Model evaluation: evaluates with RMSLE
README.md - XGBoost model → Model evaluation: evaluates with RMSLE
README.md - CatBoost model → Weighted ensemble: contributes predictions
README.md - LightGBM model → Weighted ensemble: contributes predictions
README.md - XGBoost model → Weighted ensemble: contributes predictions
README.md - Weighted ensemble → Submission generation: generates submission
README.md - Submission generation → Kaggle leaderboard: evaluated on
README.md