Tables with actual results: https://docs.google.com/spreadsheets/d/1pNlDeTu0y6qOa4XepUx7wDxxQo-HiY_8ywSdLeCqohc/edit?usp=sharing

1. Data overview and preprocessing

The dataset has the following structure:

Meanings of the columns:

• Ship.to - restaurant id
• PLZ - postal code of the restaurant
• Material - material id that was ordered
• Delivery_Date_week - date of the order delivery
• Status - status of the restaurant
• MaterialGroup.1, MaterialGroup.2, MaterialGroup.3 - characteristics of the material
• Amount_HL - amount of ordered material

Steps of data preprocessing:

1. Add two columns with day and month of order (Day, Month)
2. Ordinal encoding of categorical features (Ship.to, PLZ, Material, Day, Month, Status, MaterialGroup.1, MaterialGroup.2, MaterialGroup.4)
3. There are cases when several different materials are ordered at the same day by one restaurant. For example:

Such rows are combined into a single one by summing up Amount_HL and joining the values of Material, MaterialGroup.1, MaterialGroup.2 and MaterialGroup.4 features

4. Add column dt which is responsible for time difference (in days) between consecutive orders for each restaraunt

The resulting dataset has the following form:

2. Train-test split

Train data contains history of orders from 2012-12-30 till 2017-12-31 (periods with available information are different for each particular restaurant). Test data provides infromation on orders from 2018-01-07 till 2018-07-15 for each restaurant that is present in the train data.

3. Feature processing for training/testing the classic Machine Learning models

In order to train and test the classic Machine Learning models, we need to process the features in an appropriate manner.

• Features Ship.to, PLZ, Day, Month, Status are encoded using a one-hot encoding scheme
• Features Material, MaterialGroup.1, MaterialGroup.2, MaterialGroup.4 are turned into vectors with Bag of Words procedure
• Features dt, Amount_HL, Material, MaterialGroup.1, MaterialGroup.2, MaterialGroup.4 are scaled with Standard Scaler

The main task is to predict some information in a particular day using information from several prior timestamps. The parameter look_back is responsible for the number of timestamps which can be used to construct the prediction for the following timestamp. If look_back is equal to 4, then we take 4 rows that relate to one restaurant and concatenate them into one row (features Ship.to and PLZ are appeared only one time). The target variable might be some value from the next row (that follows these 4 considered rows).

4. Regression problem

In the regression problem we are interested in prediction of Amount_HL. So, for each restaurant we use all information (including Amount_HL) from several subsequent timestamps and predict the value of Amount_HL for the next timestamp.

4.1 Baseline

In the baseline model we actually need only the test set and feature processing step can be skipped. To predict the Amount_HL value for a particular timestamp, we average previous values of Amount_HL (the number of values that we average are specified by look_back parameter).

To launch the baseline model:

python3 models/regression/baseline.py

4.2 XGBoost

For this model all features are processed by the way described above.

To launch the XGBoost model:

python3 models/regression/xgboost.py

r2_score for the test set will be saved in the results/ folder