This repository presents a Data Science project developed as part of a 6-month Bootcamp in the field. The project utilizes retail data to perform data cleaning and preprocessing, exploratory data analysis (EDA), RFM analysis, and training of three models: Customer Segmentation, Recommendation System, and Sales Prediction
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Customer Clustering: This model segments customers into distinct groups based on their purchasing behavior.
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Product Recommendation System: This system leverages collaborative filtering with implicit feedback to recommend products to customers.
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Sales Prediction Model: This model aims to predict future sales; however, the dataset was found to be insufficient to achieve satisfactory results.
Dataset: The dataset used in this project is a retail dataset containing customer purchase history data. https://www.kaggle.com/datasets/divanshu22/online-retail-dataset/data
Key Technologies:
- Data cleaning and preprocessing
- Exploratory data analysis (EDA)
- RFM analysis
- Customer clustering
- Collaborative filtering
- Sales prediction modeling
Additional Notes:
- This project serves as a practical demonstration of Data Science skills acquired during a 6-month Bootcamp.
- The project showcases the application of various Data Science techniques to retail data.
- The project provides a valuable learning resource for aspiring Data Scientists.