E-Commerce Sales Insights Dashboard

Karanveer Sharma

Data Analyst

pandas

SQL

Streamlit

E-Commerce

📊 E-Commerce Sales Insights Dashboard

🚀 Project Overview

This project leverages SQL, Pandas, and Streamlit to analyse e-commerce sales data, providing insights into trends, revenue distribution, and top-performing products/categories. The objective is to enhance decision-making using data-driven strategies.

   ![image](https://github.com/user-attachments/assets/bd9593f4-2f5a-4be5-b031-fc6671b75282)

📂 Technologies Used

SQL (Data Extraction & Analysis)

Pandas (Data Cleaning & Processing)

Streamlit (Interactive Dashboard & Visualisation)

Matplotlib (Additional Data Visualisations)

🔍 Key Features & Insights

1️⃣ Data Extraction & Preprocessing

Query sales data from an SQLite database.

Filters data from 2023 onwards for relevant insights.

Computes total sales for each product.

2️⃣ Top 5 Best-Selling Categories

Aggregate revenue per category.

Order categories are based on the highest total sales.

Uses bar charts for visual representation.

3️⃣ Monthly Revenue Trend Analysis

Extracts monthly revenue trends using strftime('%Y-%m', order_date).

Generates interactive line charts for visualisation.

4️⃣ Automated Business Reports

Summarises key sales insights.

Stores results in sales_report.txt.

Uses Streamlit alerts to notify users.

📊 Sample SQL Queries

✅ Best-Selling Categories Query

SELECT category, SUM(quantity * unit_price) AS total_revenue
FROM sales_data
GROUP BY category
ORDER BY total_revenue DESC
LIMIT 5;

✅ Monthly Revenue Trend Query

SELECT strftime('%Y-%m', order_date) AS month,
       SUM(quantity * unit_price) AS total_revenue
FROM sales_data
GROUP BY month
ORDER BY month;

🏗️ Future Enhancements

✅ Implement predictive analytics using Machine Learning 🤖 ✅ Add real-time data updates for dashboards 🔄 ✅ Optimise SQL queries for faster performance 🚀

📎 How to Run the Project

1️⃣ Clone the Repository:

git clone https://github.com/your-repo/ecommerce-sales-dashboard.git

2️⃣ Install Dependencies:

pip install pandas sqlite3 streamlit matplotlib

3️⃣ Run the Streamlit Dashboard:

streamlit run app.py

🌟 Connect With Me

🚀 LinkedIn 📂 GitHub 💬 #DataAnalytics #SQL #Pandas #Streamlit #EcommerceInsights

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Posted Jul 10, 2025

Developed an e-commerce sales insights dashboard using SQL, Pandas, and Streamlit.

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