Data Analysis on Superstore Sales Dataset

Project Overview

This project focuses on analyzing the data from a superstore. It consists of four main sections:

1. Data Warehouse Preparation:

   • Preprocess and clean the data.
   • Design the data warehouse structure with Fact and Dimension tables.
   • Create keys and relationships between tables.
   • Import tables into Power BI and ensure correct relationships.

2. Statistical Analysis:

   • Investigate the impact of discounts on sales.
   • Divide the data into discount and non-discount groups.
   • Analyze the distribution of sold items in each group.
   • Perform statistical tests to determine if there is a significant difference between the groups.

3. Machine Learning:

   • Task 1: Profit Estimation

     • Preprocess the data and select relevant features.
     • Train a model to predict the profit of a sold product.
     • Ensure the model is generalizable and avoids overfitting.

   • Task 2: Shipping Mode Prediction

     • Preprocess the data and select appropriate features.
     • Train a model to predict the shipping mode for each order.
     • Evaluate the model's performance.

4. Dashboard Creation:

   • Import the preprocessed data into Power BI.
   • Design and create a dashboard with meaningful visualizations.
   • Provide recommendations for improving the business based on the analysis.

Contributing

Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request.

Acknowledgments

• Special thanks to Power BI for data visualization and analysis capabilities.
• The scikit-learn library was used for machine learning tasks.
• Sina Asghari and ArsalanMoravvej, [Sina Saberi], Mohammad Norasteh for their contributions and efforts in developing this project.