Machine Learning exercise
The objective is to predict the revenue of shops.
5 hours to deliver the results
Group of 2: B.M.Pardelhas, Luciefley
Dataset was given during class. (640840 rows X 9 columns)
See Data_description.txt
- Dataset exploration
- Data cleaning
- Selecting the model
- Trainning + testing the model
- Improving Predictions, Feature engineering
- Delivering the results
- Data visualization : correlation matrix, heatmap, pairplots - [Matplotlib, Seaborn]
- Pycaret
- Model : xgboost (extreme gradient boosting)
and output
To get better predictions, we should have trained the model on the opening days only. Separating stores according to size (large, medium, small), and flagging december and summer months can help improve the score also.