🛍️ Sales Data Analysis using Python

📘 Project Overview

This project analyses e-commerce sales data to identify key business insights such as:

• Most sold products
• Best performing cities
• Monthly and hourly sales trends
• Relationship between quantity and revenue

🧠 Objective

To demonstrate data cleaning, analysis, and visualisation skills using Python, Pandas, NumPy, and Matplotlib.

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🧰 Tools & Libraries

• Python 3
• Pandas
• NumPy
• Matplotlib
• Jupyter Notebook

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📊 Dataset

The dataset contains sales information, including:

• Order ID
• Product
• Quantity Ordered
• Price Each
• Order Date
• Purchase Address
• Month
• Sales
• City
• Hour

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📈 Key Insights

• Top-selling product: Determined using grouped sales quantities.
• Most profitable city: Calculated using total sales.
• Monthly trends: Identified peak sales months.
• Hour analysis: Found the best time of day for orders.

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🖼️ Visualizations

• Bar chart of Top 10 Most Sold Products
• Sales comparison by City
• Monthly sales trend

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💡 Conclusion

This project helped explore real-world business questions using Python.
It showcases strong skills in:

• Data Cleaning
• Exploratory Data Analysis (EDA)
• Visualization
• Insight Reporting

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📁 Files in Repository

File	Description
sales Data.ipynb	Jupyter Notebook with complete code
sales data.csv	Dataset used for analysis
README.md	Project documentation

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🧑‍🎓 Author

NITHIN.Y Aspiring Data Analyst | Python & SQL Enthusiast
www.linkedin.com/in/46nithin