Demand Forecasting on Corporación Favorita Grocery Sales Data

Code

1. Sampling_dataset.py: Sampling and preprocessing dataset

2. Parameter_tunning_GBDT.py: Hyperparameter tunning on Gradient Boost Trees based model

3. Parameter_tunning_DNN.py: Hyperparameter tunning on Deep Neural Network based model

4. Train_GBDT.py: Training, validation and testing on Gradient Boost Trees based model

5. Train_DNN.py: Training, validation and testing on Deep Neural Network based model

6. Train_GBDT_perday.py: Training, validation and testing on Gradient Boost Trees based models for each day of the week

7. Train_DNN_perday.py: Training, validation and testing on Deep Neural Network based models for each day of the week

8. Visualise_Data.py: Daraw several visualisations used in the presentation

Data: obtained from kaggle

Required Python libraries: Numpy, Pandas, Sklearn, Tensorflow, Keras, Lightgbm, Matplotlib