Sales Analytics Project

Overview

This project analyzes sales data to extract actionable insights. Using Python's data analysis and visualization libraries, the project explores key metrics such as total sales, regional performance, and product trends. The goal is to provide a comprehensive understanding of the sales dynamics and identify areas for improvement.

Features

• Data Cleaning: Handled missing values and corrected data types to ensure the dataset is ready for analysis.
• Exploratory Data Analysis: Performed in-depth analysis to understand data distributions and correlations among different variables.
• Visualization: Generated insightful graphs to represent sales trends and regional performance.

Visualizations

Sales Distribution by Gender

Total Amount Spent by Age Group:

Top 5 States by Sales Amount:

Sales by Product Category:

Occupation vs. Sales Amount:

Technologies Used

• Python: The primary programming language used for data analysis.
• Pandas: For data manipulation and analysis.
• Matplotlib: For creating static, animated, and interactive visualizations in Python.
• Seaborn: For making statistical graphics.

How to Run

1. Clone the repository to your local machine:

   git clone https://github.com/yourusername/Sales_Analytics.git
   

2. Install the required Python libraries:

   pip install -r requirements.txt
   

3. Run the Jupyter notebook to see the analysis and visualizations

   jupyter notebook Sales_Analytics.ipynb
   

Conclusion

This Sales Analytics project provides valuable insights into sales trends and regional performance, offering a solid foundation for data-driven decision-making in sales strategy.

License

This project is licensed under the MIT License - see the LICENSE file for details.