AI-Powered Sales Forecasting & Decision Support System

Predict future sales trends, identify demand surges, and enable data-driven business decisions using Machine Learning, Time-Series Forecasting, and Interactive Analytics.

Live Demo GitHub Repository Power BI Dashboard

Website & Project Demonstration

Complete walkthrough of the sales forecasting system including frontend workflow, prediction engine, and outputs.

Website Screenshots

Business Problem

Businesses frequently struggle with inventory planning and demand estimation due to fluctuating sales patterns and seasonal trends. Traditional methods often rely on historical averages and manual assumptions.

Overstocking products and increasing storage costs
Understocking high-demand products
Missing seasonal demand spikes
Poor business decision making

Our Solution

This project introduces an AI-driven Sales Forecasting and Decision Support System that leverages Machine Learning and Time-Series analysis to forecast future sales trends across product categories and customer states.

Key Features

Multi-Week Sales Forecasting

Generate future weekly predictions for products and states.

Demand Risk Intelligence

Estimate demand increase or decrease probabilities.

Interactive Dashboard

Power BI dashboards with KPI monitoring.

Automated ML Pipeline

End-to-end preprocessing and prediction workflow.

Seasonal Event Detection

Identify Black Friday and Christmas spikes.

Scalable Architecture

Production-ready modular architecture.

System Architecture

Raw Dataset ↓
Data Cleaning Pipeline ↓
Feature Engineering ↓
Machine Learning Training ↓
Forecast Engine ↓
Streamlit Application ↓
Power BI Dashboard ↓
Business Decision Support

Technology Stack

Programming

Python

Machine Learning

Scikit-Learn
XGBoost
SciPy

Database

PostgreSQL

Visualization

Power BI
Matplotlib

Deployment

Streamlit

Business Intelligence Dashboard

Monitor and analyze sales performance across multiple dimensions.

Power BI Screenshots

How It Works

Step 1: Load sales data ↓
Step 2: Generate time-series features ↓
Step 3: Train model ↓
Step 4: Predict future sales ↓
Step 5: Calculate risk probabilities ↓
Step 6: Display dashboards

Business Impact

Improve inventory planning
Reduce stock shortages
Detect seasonal demand spikes
Improve forecasting accuracy
Enable strategic decisions