Sweet-Lift-Taxi-Time-Series-Predictions

Skills Demonstrated

Pandas
Visualizations
Time Series Data
Seasonal Decompose
Regression Models
XGBoost
CatBoost
Light GBM

Purpose

Sweet Lift Taxi company has collected data on taxi orders at airports. Their aim is to predict the amount of taxi orders for the next hour, in order to allocate more drivers for peak hours. We will build a model with an RMSE lower than 48.

Conclusions

Overall, we succeeded in providing a model for Sweet Lift Taxi to predict the number of orders of the next hour. The target metric for our model was an RMSE score under 48. Our final model was a voting regressor, with a final RMSE of 46.47 with the test data set. Therefore, Sweet Lift can accommodate drivers with a model that accurately predicts future number of orders.

Sweet Lift