📈 Sales Analysis Project (Python + EDA)

📊 Project Overview

This project is an Exploratory Data Analysis (EDA) of the Superstore Sales dataset using Python.
The goal is to analyze sales performance, identify trends, and visualize key business metrics.

It covers sales growth over time, profitability by product and region, and the relationship between sales and profit.

📦 Dataset: Superstore Sales Dataset

---

🛠️ Tools & Libraries

• Python 3.11
• pandas — data manipulation and cleaning
• numpy — numerical analysis
• matplotlib & seaborn — data visualization
• Jupyter Notebook — interactive analysis environment

---

❓ Key Questions

1. How have sales changed over time?
2. Which products and categories bring the most profit?
3. Which region is the most profitable?
4. What are the best-selling subcategories?
5. Is there a correlation between sales and profit?

---

🧠 Key Insights

• 📈 Sales show consistent growth over time.
• 🧾 Office Supplies and Technology are the most profitable categories.
• 🌎 The West region generated the highest profit margin.
• 📊 Sales and Profit have a positive correlation, with some noticeable outliers.
• 💡 Discounts above 30 % negatively affect profit margins.

---

🧩 Project Structure

Sales_Analysis_Project/ ├─ data/ │ └─ superstore_sales.csv ├─ notebooks/ │ └─ sales_analysis.ipynb ├─ images/ │ ├─ plots.png │ └─ dashboard_preview.png └─ README.md

---

⚙️ How to Run

1. Clone this repository

   git clone https://github.com/BlladeRunner/Sales_Analysis_Project.git
   cd Sales_Analysis_Project 
   python -m venv .venv
   .\.venv\Scripts\activate
   pip install -r requirements.txt
   jupyter notebook

Then open sales_analysis.ipynb and run all cells.

💼 Business Relevance

• The analysis provides insights that help retail and e-commerce managers:
• Identify the most profitable product segments and regions.
• Optimize discount strategies to maximize profit.
• Forecast sales trends to support inventory and marketing decisions.

🧰 Skills Demonstrated

Category	Details
Data Analysis	Cleaning, aggregation, grouping, correlation
Visualization	Matplotlib, Seaborn, trend analysis
Business Analytics	Profitability, AOV, discount effect
Tools	Python, Jupyter, GitHub

🔙 Back to Portfolio