https://github.com/errhythm/nyctaxifarepred-extended
NYC Taxi Fare Prediction with 7 models (Linear Regression, Random Forest, XGBoost, LightGBM, CatBoost, KNN, and Decision Tree) The models used range from simple linear regression to more complex ensemble methods such as boosting algorithms. The aim was to improve prediction accuracy and handle categorical features efficiently.
https://github.com/errhythm/nyctaxifarepred-extended
catboost decision-tree ensemble-model knn lightgbm nyc-taxi-dataset regression xgboost
Last synced: about 1 year ago
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NYC Taxi Fare Prediction with 7 models (Linear Regression, Random Forest, XGBoost, LightGBM, CatBoost, KNN, and Decision Tree) The models used range from simple linear regression to more complex ensemble methods such as boosting algorithms. The aim was to improve prediction accuracy and handle categorical features efficiently.
- Host: GitHub
- URL: https://github.com/errhythm/nyctaxifarepred-extended
- Owner: errhythm
- Created: 2023-02-09T12:29:20.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-02-14T14:01:44.000Z (over 3 years ago)
- Last Synced: 2025-04-05T14:34:42.911Z (about 1 year ago)
- Topics: catboost, decision-tree, ensemble-model, knn, lightgbm, nyc-taxi-dataset, regression, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 1020 KB
- Stars: 2
- Watchers: 1
- Forks: 2
- Open Issues: 0