Project Overview:

The Retail Sales Dashboard with Predictive Analytics is a web-based system that allows businesses to analyze historical sales data, monitor real-time performance, and forecast future sales using machine learning models. It helps retailers make data-driven decisions by providing interactive charts, trend analysis, category-wise breakdowns, and sales predictions for upcoming weeks or months.

This project combines data science, big data visualization, and forecasting models to improve business intelligence in the retail sector.

Key Features:

1.  Interactive dashboard for daily/monthly/yearly sales

2.  Category-wise and product-wise sales breakdown

3.  Region/branch-level performance comparison

4.  Forecast future sales using ML models (ARIMA, Prophet, etc.)

5. Insights into top-selling and low-performing products

6.  Real-time data updates or CSV import option

7.  Admin panel to upload or manage sales data

Technology Stack:

 Backend (choose one):

• PHP + MySQL

• Node.js + MongoDB

• Java (Spring Boot) + PostgreSQL

 Frontend:

• HTML, CSS, Bootstrap

• JavaScript (Chart.js, DataTables, AJAX)

• Optional: D3.js for advanced visualizations

 Data Analytics & Forecasting (Python):

• Libraries: Pandas, NumPy, Matplotlib, Scikit-learn, Prophet, ARIMA

• Optional Big Data Tools: Apache Spark, Kafka (for real-time analytics)

Workflow:

1. Data Collection:

   • Historical sales data is uploaded via CSV or auto-fetched from DB

   • Includes fields like product ID, date, store location, units sold, revenue

2. Dashboard & Analytics:

   • Generate key metrics: revenue, average basket size, etc.

   • Graphs for trends over time, peak shopping hours, location performance

3. Forecasting Module:

   • Use ML models (Prophet, ARIMA, XGBoost) to predict future sales

   • Can forecast for individual products or entire categories

4. User Interface:

   • Filter and search by category, time period, region

   • Export graphs and sales reports