This project analyzes price history and discount history datasets to uncover insights into discount frequency across stores and the highest-priced items. The analysis involves data cleaning, merging, and exploratory data analysis (EDA) to answer key business questions.
1️⃣ Which items (item_id) are discounted most frequently, and in which stores (store_id)?
2️⃣ What are the top 10 highest-priced items?
✔ Data Sources:
- Manually downloaded data
- KaggleHub API for automated retrieval
✔ Data Wrangling Steps:
- Cleaned missing and inconsistent values
- Merged price history and discount history datasets
- Python (Pandas, NumPy)
- Data Visualization: Matplotlib
- API Usage: KaggleHub API
- Jupyter Notebook for analysis