E-Commerce Data Analysis

This repository contains Python scripts for analyzing e-commerce transactional data. The analysis includes filtering, summarizing, and visualizing key metrics to gain actionable insights.

Key Features

• Data Cleaning:

  • Identified and removed invalid entries.
  • Replaced missing values with placeholders for categorical data.

• Exploratory Data Analysis (EDA):

  • Bar graphs for categorical variables like user level, gender, education, etc.
  • Line plots for daily and hourly order trends.

• Business Insights:

  • Identified peak order days and hours.
  • Analyzed the distribution of product prices and sales quantities.

• Key Metrics:

  • Orders with multiple packages were filtered.
  • Top price range with the highest orders identified.

Visualizations

• Daily and hourly trends of quantity sold.
• Distribution of product prices (original_unit_price and final_unit_price).
• Bar graphs for user demographic distributions.

Dataset Information

The analysis is based on anonymized e-commerce transactional data with the following key tables:

• User Data
• Order Data
• Delivery Data
• Inventory Data
• Network Data

Future Enhancements

• Add predictive models for sales forecasting.
• Integrate dashboards for real-time monitoring.