The TerraStore Recommender System is an AI-powered application designed to enhance the marketing strategy of Terra Store, an e-commerce company. The system predicts customer purchase behavior based on historical data and provides insights into which products a customer is likely to purchase next.
- Predicts the next product a customer is likely to buy.
- Provides personalized recommendations based on customer interactions and purchase history.
- User-friendly web interface for easy interaction.
- Description: This recommender system ranks products based on their overall ratings and recommends the top-rated products to users.
- Methodology: Products are ranked by their average ratings, and the top-ranked products are recommended to users.
- Implementation: Implemented using collaborative filtering techniques such as Singular Value Decomposition (SVD)++ and evaluated by RMSE and MAE value provided by surprise libraries.
- Clone the repository:
git clone https://github.com/mulkiah/recommender_system.git
cd recommender_system
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Install dependencies:
pip install -r requirements.txt -
Run the web application:
streamlit streamlit run code/app.py
This project is licensed under the MIT License. See the LICENSE file for det