📊 Smart Sales Intelligence Dashboard

This is an interactive, end-to-end Sales Intelligence Platform built using Python, Streamlit, SQLite, and Pandas. It allows users to explore synthetic sales data through a visually engaging dashboards, apply filters, search queries, and download custom reports — all in real time.

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🔗 Live App URL

Streamlit App: https://salesintelligenceplatform-ztvbawwvr74weynohuzuhl.streamlit.app/

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💡 Project Overview

This platform simulates how businesses can monitor and analyze sales performance. From high-level KPIs to customer-level drill-downs, this project demonstrates key business intelligence capabilities, including:

• Revenue insights
• Product and customer analytics
• Region-wise performance
• Real-time data exploration
• Downloadable custom datasets

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📦 Features

• 🔍 Search + Filters: Filter by customer, product, region, brand, or date range
• 📈 Revenue Trends: Line chart of monthly revenue
• 🏆 Top Products & Customers: Bar charts of top 5 products and customers
• 🌍 Region Analysis: Revenue distribution by region
• 👥 Gender Distribution: Pie chart visualization
• 📋 Live Data Table: Filtered data preview
• ⬇️ Download CSV: Export filtered data in one click

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🧰 Tech Stack

• Python
• Streamlit
• SQLite
• Pandas
• Matplotlib / Seaborn
• Faker (for synthetic data)

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🗂️ Folder Structure

Sales_Intelligence_Platform/
├── app/
│   ├── dashboard.py         
│   └── etl.py               
│
├── data/
│   ├── dim_customer.csv    
│   ├── dim_product.csv     
│   └── fact_sales.csv       
│
├── db/
│   └── sales.db            
│
├── notebooks/
│   └── eda.ipynb            
│
├── sql/
│   └── create_tables.sql    
│
├── .streamlit/
│   └── config.toml          
│
├── requirements.txt         
└── README.md               

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⚙️ How to Run Locally

Step 1: Clone the repository

git clone https://github.com/Pratikchetry/Sales_Intelligence_Platform.git
cd Sales_Intelligence_Platform

Step 2: Install dependencies

pip install -r requirements.txt

Step 3: Generate dummy data and SQLite database

python3 app/etl.py

Step 4: Run the Streamlit dashboard

streamlit run app/dashboard.py

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📈 Dashboard Snapshots

• Monthly Revenue Trend (Line chart)
• Top 5 Products & Customers (Bar charts)
• Sales by Region (Bar chart)
• Customer Gender Split (Pie chart)
• Interactive Data Table with Download Option

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📌 Future Enhancements

• Connect to real-time API or cloud database
• Add forecast/prediction using ML
• Add login/authentication features
• Improve UI responsiveness for mobile

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👤 About the Author

Name: Pratik Chetry GitHub: https://github.com/Pratikchetry LinkedIn: https://www.linkedin.com/in/pratik-chetry/ Email: chetrypratik2002@gmail.com

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📜 License

This project is open-source and free for personal or academic use.