AI-Assisted Sales Intelligence Dashboard Project Overview This project is an end-to-end Sales Analytics solution built using Python, SQL, and Power BI.

The workflow includes:

Data Cleaning using Python (Pandas) Data Storage using SQLite Business Analysis using SQL Queries Interactive Dashboard using Power BI Automated Business Report Generation using Python The goal of this project is to analyze sales performance, identify profitable products, monitor regional performance, and generate business recommendations.

Dataset Dataset: Sample Superstore Dataset

Records: 9,994

Features:

Order Details Customer Information Product Information Sales Profit Discount Region Category Sub-Category Tools & Technologies Python Pandas SQLite SQL Power BI Data Visualization Business Intelligence Project Workflow CSV Dataset

↓

Data Cleaning (Python)

↓

SQLite Database

↓

SQL Analysis

↓

Power BI Dashboard

↓

AI Business Report

Dashboard Pages

Executive Overview Total Sales Total Profit Total Orders Profit Margin Regional Sales Analysis Category Performance Analysis

Product Performance Analysis Top Profitable Products Loss-Making Products Profit by Category Sales by Sub-Category

Sales Trends Analysis Sales Trends Over Time Profit Trends Seasonal Performance Insights Key Business Insights West Region generated the highest sales. Technology is the most profitable category. Several products generate significant losses and require pricing review. Sales performance varies significantly across regions. Automated AI Report The project includes a Python-based reporting system that automatically generates:

Total Sales Total Profit Business Insights Strategic Recommendations

Author Gourav Jangid

Aspiring Data Analyst

Open to Data Analytics Opportunities

Manufacturing ERP & Production Intelligence Dashboard

The dark plant nav next to the light data canvas keeps the menus from fighting the charts. Putting the packing trend beside stock balance makes the floor numbers readable at a glance.