Computing predictions for specific dates and time horizons

The plan for replicating Monsaraida’s solution is to create a notebook customizable by input parameters to produce the necessary processed data for training and test datasets and the LightGBM models for predictions. The models, given data in the past, will be trained to learn to predict values in a specific number of days in the future. The best results can be obtained by having each model learn to predict the values in a specific week range in the future. Since we have to predict up to 28 days ahead, we need a model predicting from day +1 to day +7 in the future, then another one able to predict from day +8 to day +14, another from day +15 to +21, and finally, another one capable of handling predictions from day +22 to day +28. We will need a Kaggle notebook for each of these time ranges, thus we need four notebooks. Each of these notebooks will be trained to predict the future time span for each of the 10 stores that...