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# Cross-Sell Up-Sell Recommender System | ||
A recommender system that enables cross sell and upsell of products (either new products or already bought products) that will enable higher revenue generation. The data captures material that is sent to the wholesalers over a span of time. | ||
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[![](Sample.png)](https://drive.google.com/file/d/1n7JeZZ4E5O_HIuUbOQGvoNWYFVpzAHEV/view?usp=sharing) | ||
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## Objectives | ||
* [x] Finding how similar other wholesalers are to W1. | ||
* [x] Finding wholesalers who buy similar products. | ||
* [x] Finding wholesalers who buy similar products. | ||
* [x] Application of a feedback mechanism so that sales team can easily implement their knowledge of incompatible products/customer. | ||
* [x] To find which products are popular. | ||
* [x] To recommend the product which will enable higher revenue generation. | ||
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### The dataset | ||
The dataset has been provided by AB InBev as a part of their hackathon Maverick 2.0: Hack-a-thon where the participants in the Cross-Sell Up-Sell track were to build a Recommender System based on the data given Consumers/Wholesalers and their history of purchase. | ||
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### Installation and setup | ||
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1. Open terminal and come to the main directory. | ||
2. Input the following commands by creating a new conda environment: | ||
conda create --name recsys python=3.8.5 | ||
conda activate recsys | ||
pip install -r requirements.txt | ||
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## Contributing | ||
Feel free to contribute features / point out errors. Fork this repository and make a pull request. |
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