# Walmart Sales Forecasting Competition: SQL Analysis
## Introduction
Diving into the Walmart Sales Data, this project aims to extract patterns, trends, and actionable insights through comprehensive SQL analysis. From data wrangling to feature engineering, the endeavor progresses through enhancing the dataset with new attributes, setting the stage for an in-depth exploratory data analysis (EDA). Utilizing SQL's robust features, such as aggregation, conditional logic, and grouping, the project dissects sales performance across multiple dimensions—product lines, customer types, and time frames. This initiative not only demonstrates my SQL prowess but also emphasizes my analytical skills in distilling complex data into meaningful insights.
## Objectives
- **Data Wrangling:** Clean and structure the dataset for analysis.
- **Feature Engineering:** Enhance the dataset with new, insightful attributes.
- **Exploratory Data Analysis:** Investigate sales performance through SQL queries, focusing on various business dimensions.
## Key Highlights
- Applied SQL techniques for data analysis, including data manipulation, retrieval, grouping, and ordering.
- Employed conditional aggregation and subqueries to analyze data segments and derive insights.
- Analyzed sales data across product lines, customer types, and temporal factors, highlighting sales performance and trends.
## Skills Demonstrated
- **SQL Proficiency:** Showcased through data manipulation, analysis, and query optimization.
- **Analytical Acumen:** Ability to derive actionable insights from complex datasets.
- **Problem-Solving:** Employed advanced SQL features and logical reasoning to tackle data analysis challenges.
## Collaboration
This project was developed in collaboration with [Shazab Hassan](https://github.com/shazabhassan), whose insights and expertise significantly contributed to the depth and scope of the analysis.
## Tools and Resources
- **Data Source:** [Kaggle Walmart Sales Forecasting Competition](https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting)
- **Learning Resources:** [GitHub - WalmartSalesAnalysis](https://github.com/Princekrampah/WalmartSalesAnalysis)
## Installation and Setup
Ensure MySQL or a compatible SQL database system is installed. Clone the repository and import the dataset into your database environment. Follow the SQL scripts provided for data analysis.
## Usage
The project is structured into SQL scripts that sequentially walk through data preparation, feature engineering, and analysis. Execute the scripts in your SQL environment to replicate the analysis and explore the sales data.
## Contributions and Feedback
Contributions to improve the analysis or extend the project are welcome. Please feel free to fork the repository, make your changes, and submit a pull request. For feedback or suggestions, open an issue in the repository.
## Acknowledgments
Gratitude to Kaggle for providing the Walmart Sales Data and to the community contributors for their valuable insights that inspired this project.
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## About the Author
Ryan M. Gichuru, a passionate data analyst, showcases his SQL skills and analytical capabilities through this project. With a keen interest in leveraging data for business insights, Ryan continues to explore and contribute to the field of data analytics.This README file provides a comprehensive overview of your GitHub project, highlighting the process, objectives, skills, and usage. It's structured to serve both as a documentation for the project and an insightful guide for the GitHub community.