Walmart Data Analysis: End-to-End SQL + Python Project P-9

Project Overview

![Project Pipeline](

This project provides an end-to-end analysis of Walmart sales data, focusing on extracting actionable business insights. Python is used for data preprocessing and transformations, while SQL helps perform advanced queries to address business problems. The work demonstrates skills in data manipulation, SQL analysis, and building structured data pipelines.

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Project Steps

1. Set Up the Environment

• Tools Used:Visual Studio Code (VS Code), Python, MySQL, PostgreSQL
• Goal: Created a structured workspace in VS Code with organized folders for smooth development.
• Ensured a clean setup for handling data and scripts.

2. Set Up Kaggle API

• API Setup: Obtain your Kaggle API token from Kaggle by navigating to your profile settings and downloading the JSON file.
• Configure Kaggle:
  • Place the downloaded kaggle.json file in your local .kaggle folder.
  • Use the command kaggle datasets download -d <dataset-path> to pull datasets directly into your project.

3. Download Walmart Sales Data

• Data Source: Use the Kaggle API to download the Walmart sales datasets from Kaggle.
• Dataset Link: Walmart Sales Dataset
• Storage: Save the data in the data/ folder for easy reference and access.

4. Install Required Libraries and Load Data

• Libraries: Install necessary Python libraries using:

  pip install pandas numpy sqlalchemy mysql-connector-python psycopg2

• Loading Data: Read the data into a Pandas DataFrame for initial analysis and transformations.

5. Explore the Data

• Goal: Conduct an initial data exploration to understand data distribution, check column names, types, and identify potential issues.
• Analysis: Use functions like .info(), .describe(), and .head() to get a quick overview of the data structure and statistics.

6. Data Cleaning

• Remove Duplicates: Identify and remove duplicate entries to avoid skewed results.
• Handle Missing Values: Drop rows or columns with missing values if they are insignificant; fill values where essential.
• Fix Data Types: Ensure all columns have consistent data types (e.g., dates as datetime, prices as float).
• Currency Formatting: Use .replace() to handle and format currency values for analysis.
• Validation: Check for any remaining inconsistencies and verify the cleaned data.

7. Feature Engineering

• Create New Columns: Calculate the Total Amount for each transaction by multiplying unit_price by quantity and adding this as a new column.
• Enhance Dataset: Adding this calculated field will streamline further SQL analysis and aggregation tasks.

8. Load Data into MySQL and PostgreSQL

• Set Up Connections: Connect to MySQL and PostgreSQL using sqlalchemy and load the cleaned data into each database.
• Table Creation: Set up tables in both MySQL and PostgreSQL using Python SQLAlchemy to automate table creation and data insertion.
• Verification: Run initial SQL queries to confirm that the data has been loaded accurately.

9. SQL Analysis: Complex Queries and Business Problem Solving

• Business Problem-Solving: Write and execute complex SQL queries to answer critical business questions, such as:
  • Revenue trends across branches and categories.

  SELECT city, category,
       MIN(rating) as min_rating,
       MAX(rating) as max_rating,
       AVG(rating) as avg_rating
  FROM walmart
  GROUP BY 1, 2

 - Identifying best-selling product categories.
 - Sales performance by time, city, and payment method.
 - Analyzing peak sales periods and customer buying patterns.
 - Profit margin analysis by branch and category.

• Documentation: Keep clear notes of each query's objective, approach, and results.

10. Project Publishing and Documentation

• Documentation: Maintain well-structured documentation of the entire process in Markdown or a Jupyter Notebook.
• Project Publishing: Publish the completed project on GitHub or any other version control platform, including:
  • The README.md file (this document).
  • Jupyter Notebooks (if applicable).
  • SQL query scripts.
  • Data files (if possible) or steps to access them.

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Requirements

• Python 3.8+
• SQL Databases: MySQL, PostgreSQL
• Python Libraries:
  • pandas, numpy, sqlalchemy, mysql-connector-python, psycopg2
• Kaggle API Key (for data downloading)

Getting Started

1. Clone the repository:

   git clone <repo-url>

2. Install Python libraries:

   pip install -r requirements.txt

3. Set up your Kaggle API, download the data, and follow the steps to load and analyze.

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Project Structure

|-- data/                     # Raw data and transformed data
|-- sql_queries/              # SQL scripts for analysis and queries
|-- notebooks/                # Jupyter notebooks for Python analysis
|-- README.md                 # Project documentation
|-- requirements.txt          # List of required Python libraries
|-- main.py                   # Main script for loading, cleaning, and processing data

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Results and Insights

This section will include your analysis findings:

• Sales Insights: Key categories, branches with highest sales, and preferred payment methods.
• Profitability: Insights into the most profitable product categories and locations.
• Customer Behavior: Trends in ratings, payment preferences, and peak shopping hours.

Future Enhancements

Possible extensions to this project:

• Integration with a dashboard tool (e.g., Power BI or Tableau) for interactive visualization.
• Additional data sources to enhance analysis depth.
• Automation of the data pipeline for real-time data ingestion and analysis.

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License

This project is licensed under the MIT License.

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Acknowledgments

• Data Source: Kaggle’s Walmart Sales Dataset
• Inspiration: Walmart’s business case studies on sales and supply chain optimization.

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