This project performs a detailed exploratory data analysis (EDA) on product data extracted from an Excel file (product.xlsx). The analysis aims to uncover key insights about product categories, pricing trends, customer ratings, and seller engagement using Python-based data science tools.
The analysis is organized into multiple numbered objectives, each representing a distinct analytical task. These include identifying the most common product categories, examining product rating distributions via histograms and boxplots, and evaluating the correlation between price, product rating, and similar product ratings. Additionally, pricing patterns are investigated using scatter plots and category-wise average line charts, enabling the identification of cost variation across different product categories.
- Python 3.7+
- pandas
- numpy
- matplotlib
- seaborn
- openpyxl (for reading Excel files)
The script uses matplotlib and seaborn to generate:
- Pie charts
- Histograms
- Scatter plots
- Heatmaps
- Bar and Line charts
- Box plots