📊 E-Commerce Sales Analysis Project

This project provides insights into an e-commerce store’s sales and profit performance using a dataset analyzed with Python and Pandas in a Jupyter Notebook. The goal is to explore key business questions such as sales trends, profit distribution, and category-wise performance to help inform decision-making.

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🛠️ Technologies Used

• Python 3
• Jupyter Notebook
• Pandas
• Matplotlib / Seaborn (for visualization)
• NumPy

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🔍 Problem Statements

1. 📅 Monthly Sales Analysis

   • Identify the month with the highest and lowest total sales.

2. 🗃️ Sales by Category

   • Determine which product category had the highest and lowest sales.

3. 🔍 Sales by Sub-Category

   • Perform sales analysis based on sub-categories.

4. 💰 Monthly Profit Analysis

   • Identify the most and least profitable months.

5. 📦 Profit by Category and Sub-Category

   • Compare profit margins across main categories and sub-categories.

6. 👥 Sales and Profit by Customer Segment

   • Analyze how each customer segment contributes to sales and profit.

7. 📈 Sales to Profit Ratio

   • Calculate and compare the ratio of sales to profit by segment.

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✅ Key Insights & Solutions

Question	Insight
1. Monthly Sales	Highest in November, lowest in January
2. Category Sales	Highest sales: Technology Lowest sales: Office Supplies
3. Sub-Category Sales	Phones had the highest sub-category sales
4. Monthly Profit	Most profitable month: December Least profitable: January
5. Profit by Category/Sub-Category	Highest category profit: Technology Highest sub-category profit: Courier
6. Segment Analysis	Top contributing segments: Consumer, Corporate, Home Office
7. Sales to Profit Ratio	Consumer segment has a profit ratio of 8.6

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📁 How to Run

1. Clone the repository:

   git clone https://github.com/udham31/E-commerceSalesAanalysis.git