Data Analytics Project – Ecommerce Product Rating Analysis

This project analyzes user-product ratings from an ecommerce dataset. The goal is to identify product popularity, rating trends, and user behavior through data cleaning, exploration, and visual storytelling using Python.

Dataset Used:

• File: ratings.csv (subset of ratings_Beauty.csv from Kaggle)
• Modified to contain only userId, productId, and rating columns
• Limited to 15000 records for simplicity
• Timestamp column removed

Requirements:

• Python 3.10 or higher
• pandas
• numpy
• matplotlib
• seaborn
• plotly
• jupyter notebook
• nbformat (required for interactive charts)

To install the required libraries, use: pip install pandas numpy matplotlib seaborn plotly notebook nbformat

What the Project Does:

• Loads and cleans the dataset
• Removes duplicates and missing values
• Converts rating values to numeric
• Filters out users/products with very few reviews
• Applies data transformation (log scale) for better insights

Visualizations and Insights:

• Rating distribution histogram
• Bar chart of most-rated products
• Interactive bar chart using Plotly for improved clarity
• Heatmap of sample user-product ratings
• Clear labels, legends, and color schemes added
• Data storytelling through statistics and visual patterns

How to Run:

• Open Jupyter Notebook
• Open the analysis.ipynb file inside the Notebooks folder
• Run all cells one by one to perform the analysis and see the visualizations

Author: ByteSquad