Store Sales and Analysis

Project Overview:

This project performs a comprehensive analysis of store sales and profits. By examining the dataset, it aims to identify trends, sales performance, and areas for improvement. The analysis helps in making data-driven decisions to optimize store operations, pricing, marketing, and inventory management strategies for growth and profitability.

Key Features

• Data Cleaning and Preprocessing
• Sales Trend Analysis
• Profitability Analysis
• Visualizations using Python (Matplotlib, Seaborn)
• Actionable Insights for business optimization

Dataset

The dataset used in this project is derived from a fictional superstore, covering key metrics such as sales, profit, category, discount, and more.

Tools and Libraries

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

Installation

To get started, follow these steps:

git clone https://github.com/sapritanand/Store-Sales-and-Analysis.git

Install the required dependencies:

pip install -r requirements.txt

Run the Jupyter notebook to view the analysis:

jupyter notebook StoreSalesAnalysis.ipynb

Visualizations

The project includes a wide array of visualizations to explore sales trends, product performance, and geographical insights.

Future Improvements

• Incorporate predictive modeling to forecast future sales
• Implement advanced machine learning techniques for deeper insights

Contributing

Feel free to submit issues or pull requests. Contributions are always welcome!