Business Context: You are hired as a Data Science and AI for an e-commerce company named "Terra Store." Terra Store is looking to enhance its marketing strategy by predicting customer purchase behavior based on historical data. The company wants to build an AI-powered application that can provide insights into which products a customer is likely to purchase next.

• numpy==1.26.4
• pandas==2.2.0
• scikit-learn==1.4.1
• scipy==1.12.0
• streamlit==1.31.1

Check folder datasets/ for more info.

Internal documentations can be found within the code (redirect to docs/main_ipynb.pdf).

1. All datasets were loaded and preprocessed (e.g., impute missing values, if any).
2. All tables were joined and then pivoted to create a raw distance matrix.
3. Cosine similarity was used to find correlation between items.
4. For ratings prediction,
   • The similarity scores (sim_arr) were filtered using the filtering array to keep only the scores for products that the user has rated.
   • Then, the top max_neighbor most similar products was selected based on their similarity scores.
   • The user's ratings for these top max_neighbor products were retrieved from the user_item_matrix.
   • Finally, the predicted rating was calculated as the weighted average of the user's ratings for the top similar products, where the weights are the similarity scores. The weights were then normalized by their sum to ensure they add up to 1.
5. By this, the algorithm can recommend products for a single user based on ratings (examine the function get_recommendation()).

1. Create the Python virtual environment (VENV) first: python -m venv <VENV_NAME>.
2. Activate your VENV and install all packages (from requirements.txt file).
3. To run the app, execute this command: streamlit run main.py.

Notice that the highlighted red box is for the user's inputs.

• Due to limited datasets, the result may seems inaccurate (or even nan).
• For forthcoming works, kNN can be used to measure the similarity distance between items.

• Email: [email protected]
• Portfolio: https://linktr.ee/ndominic