The document describes a fare forecasting project led by Rajesh Tamang and Dmytro Maliarenko, which aims to create an accurate fare estimation model using data from various cities. The model incorporates predictive modeling and feature engineering techniques, including weather and holiday data, and explores several machine learning algorithms, ultimately finding that LightGBM provides the best performance. The project emphasizes balancing supply and demand for ride-sharing services.

1. Project:P02. X fare forecasting
Rajesh Tamang; Dmytro Maliarenko

2. Project description
➢ Model for fare estimate, which outputs most accurate recommended fare
Dataset
Input - data of X city for Y month
Constraints - locations (pickup and dropoff)
ride distance
suburb rides
search time
seasonality
Output
Predict fare estimate, which outputs most accurate recommended fare for maximisation of supply and
demand balancing.

3. Predictive modeling Regression task

4. Feature Engineering
➢ We added weather data to our model
Source: https://www.timeanddate.com/weather/ukraine/kyiv/historic?month=8&year=2017

5. Feature Engineering
➢ Holidays

6. Feature engineering:
Clustering for
● pickup location
● dropoff location
● ride

7. Models selection
➢ We started from simple Linear Regression (mape = 12%)
➢ Explored different models:
● XGBoost
● LightGBM
● RandomForestRegressor
● ExtraTreesRegressor
LightGBM demonstrated the best result and speed among other models on the validation subset
LightGBM

8. Regression performance accuracy
LightGBM:

9. Feature importances

10. Thanks!