📊 Sales Analysis Project

🔍 Project Overview

This project analyzes sales data to uncover insights about revenue, performance across regions, and trends over time.
It was developed in Python (Jupyter Notebook) using Pandas and Matplotlib for data analysis and visualization.
The main goal is to make data speak clearly — #MAKE_DATA_TALK 🚀

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📂 Dataset

• File: large_sales_data.csv
• Columns include:
  • Date – Transaction date
  • Region – Geographic region
  • SalesPerson – Sales representative
  • Product – Product sold
  • UnitsSold – Number of units sold
  • UnitPrice – Price per unit

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🧹 Data Cleaning & Preparation

• Checked data types for each column
• Converted Date column to datetime format
• Verified no missing values (all columns clean ✅)
• Calculated a new column TotalRevenue = UnitsSold * UnitPrice

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📈 Data Analysis & Visualizations

Key insights and visuals created using Matplotlib:

1. Average Units Sold per Region – using groupby() and .mean()
2. Total Revenue per Region – using .sum() and visualized as a Pie Chart
3. Daily Revenue Trend – plotted as a Line Chart to observe changes over time

🧭 Key Visuals:

• Pie Chart → Revenue distribution per region
• Line Chart → Total daily revenue trend

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🧠 Insights

• The highest revenue was generated in top-performing regions (identified through the pie chart).
• Daily revenue trend shows fluctuations and peaks on specific dates — useful for forecasting and sales planning.
• Clean data ensured accurate aggregation and visualization.

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⚙️ Tools & Libraries Used

• Python 3
• Pandas
• Matplotlib
• Jupyter Notebook

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🗂️ Project Structure

Sales Analysis Project/
│
├── sales_analysis_project.ipynb      # Main notebook (code & analysis)
├── sales_analysis_project.html       # Exported HTML version for viewing
├── data/
│   └── large_sales_data.csv          # Original dataset
└── README.md                         # Project documentation

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🌐 How to View

• Open the .ipynb notebook in Jupyter for interactive exploration.
• Or view the HTML version directly in your browser.
• You can also preview it online using nbviewer:
  👉 https://nbviewer.org

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💬 Author

Mohamed Heta
Data Analyst | Power BI | Python | SQL
Hashtags: #MAKE_DATA_TALK #معلومة_في_السكة