📊 Superstore Sales Analysis (End-to-End Project)

🚀 Project Overview

This project analyzes sales data from a Superstore to identify key business trends. I performed a full lifecycle analysis:

1. Excel: Initial data exploration and dashboarding.
2. Python: Automated data cleaning and visualization (Pandas/Matplotlib).
3. SQL: Built an ETL pipeline to load data into MySQL and ran complex queries for validation.

🔑 Key Insights

• Technology is the best-performing category.
• Sales Trend: Consistent year-over-year growth observed from 2015 to 2018.
• Regional Analysis: The West region contributes the most to total sales.

🛠️ Tech Stack

• Languages: Python, SQL
• Database: MySQL (Connected via mysql-connector & SQLAlchemy)
• Libraries: Pandas, Matplotlib
• Tools: Jupyter Notebook, Excel

📈 Visuals

(Detailed charts available in the notebook)

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Created by Tejas Padole | Aspiring Data Analyst