E-commerce Analytics Dashboard

The E-commerce Analytics Dashboard is an interactive web application built with Streamlit and Python that transforms raw retail data into meaningful visualizations and actionable business insights. It combines descriptive analytics with predictive capabilities to help e-commerce managers, marketers, and analysts make informed decisions.

The dashboard provides a comprehensive view of sales performance, product categories, regional trends, profit margins, and customer segments. It features interactive filters, time-series visualization, and a machine learning model to predict profit based on various order characteristics.

• Interactive Data Exploration: Filter data by region, product category, and date range
• Critical KPI Metrics: At-a-glance view of total sales, profit, and average margins
• Comprehensive Visualizations: Charts and graphs for sales trends, category performance, and regional analysis
• Time Series Analysis: Track sales and profit patterns over time
• Loss Detection: Automatic identification of underperforming categories and regions
• ML-Based Predictions: Linear regression model to predict profit for new orders
• Custom Data Support: Upload your own CSV data for analysis

This project is built with the following technologies:

The application features optimized data processing with caching for improved performance, interactive data filters that update visualizations in real-time, and a machine learning pipeline using Linear Regression to predict order profitability.

The dashboard provides multiple analytical perspectives:

• Sales Performance: Total sales, time trends, and category breakdowns
• Profit Analysis: Margin calculation, profitable vs. loss-making orders
• Category Insights: Top performing products and underperforming categories
• Regional Performance: Sales by region and loss percentage analysis
• Customer Segmentation: Analysis by customer segment
• Predictive Modeling: Profit prediction based on order characteristics

Each visualization is designed to answer specific e-commerce management questions and support strategic decision-making for online retailers.