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Recommendation-System

An online clothing store aims to increase the average bill per customer, we proposed the solution as a recommender system which suggests a set of related products for customers, we tested two models and prepared a demo using Flask.

Data:

We used the data of an online clothing store https://www.kaggle.com/ruichenyang/ecommerce-clothing-store

Models:

We tested two models :

  • Baseline: Popularity based recommendation (recommend top purchased items)
  • Collaborative Filtering: Singular Value Decomposition (SVD) based Recommendation

Code Structure:

  • data_exploration.ipynb : a jupyter notebook to explore data and make some statistics
  • generate_popularity_model.py : where we create popularity model, popularity_recommender.py is used inside it
  • collaborative folder : which contains all notebooks and files to generate collaborative model
  • models folder : which contains the two saved generated models (i.e collaborative + popularity)
  • static folder: which contains the css of the demo pages
  • templates folder : it contains our html pages
  • data folder : it contains the link of our data, in addition to two csv files (orders_items + products' specification)
  • demo_screenshots folder: contains screenshots from demo

Environment :

We used google colab for data exploration,as for model building python 3.7.5, the backend was written with Flask (Python) and the frontend using Bootstrap.

To test the program just run this:

python main.py

Demo Sample

img After hitting submit, it shows the recommendations according to the selected model img There is also an error page, in case of undefined product img