How vishwajitm01/amazon-predictive-modelling-project- works
This is a data-analysis project exploring Amazon electronics sales, not a deployed commerce service. Its documented workflow uses a Kaggle dataset of more than 40,000 products to investigate two outcomes: Best Seller classification, evaluated by sensitivity because of class imbalance, and purchased-volume prediction. The README supports these stages and their purpose, but no source excerpts were available, so internal model choices, preprocessing steps, and execution links beyond the documented workflow are not represented.
Main entry points: Amazon_Project.R Analysis workflow
Components 8
Data
- Kaggle electronics data
- Product attributes
Amazon_Project.R
Predictive analysis
- Analysis workflow
Amazon_Project.R - Best Seller prediction
Amazon_Project.R - Class imbalance
Amazon_Project.R - Purchase volume prediction
Amazon_Project.R
Research outcomes
- Sensitivity evaluation
Amazon_Project.R - Stocking and promotion insights
Connections 8
- Kaggle electronics data → Analysis workflow: provides data
- Analysis workflow → Product attributes: examines features
- Analysis workflow → Best Seller prediction: builds models
- Class imbalance → Sensitivity evaluation: motivates metric
- Best Seller prediction → Sensitivity evaluation: evaluated by
- Analysis workflow → Purchase volume prediction: builds models
- Best Seller prediction → Stocking and promotion insights: informs
- Purchase volume prediction → Stocking and promotion insights: informs