RecoRelease-2

This is our second supermarket recommendation app release, containing data collection, preparation, exploration and model.

This release should be viewed in the following order (4 Steps):

Step 1:

The Data Collection/Creation, which can be viewed in the 'Projectlib/datasets', contains the 2 files that make up the whole data collection process. In order to create the final "supermarket_data.csv" file for the modelling, we followed the following process:

1. First download the csv file from https://www.kaggle.com/datasets/heeraldedhia/groceries-dataset?resource=download.
2. Then with the "DataCreation.ipynb'', execute to create the "supermarket_data.csv", which is the data that is going to be used for the recommendation system.
3. But before, we further prep the data with the "Cleaning_Preparation + EDA.ipynb" notebook.

Step 2:

The Modeling. This can be viewed in the 'Projectlib/Models' folder, and inside 2 ipynb notebooks. The first shows our algorithm attempt using collaborative filtering to recommend supermarket products and the second uses a SAR model applied based on recommeders approach.

Step 3:

The implementation of a Flask API. This can be viewed in the 'Projectlib/Flask' folder. Additionally, thee implementation of a simple front-end is created in the template folder, which is used by the ipynb file called FLaskAPI to create the local API.

Step 4:

The app implementation to deploy in azure is created and with that we add more files, necessary for the deployment using Git Actions.