Walmart Sales Analysis using Python & PostgreSQL

Project Overview

This project is an end-to-end data analysis solution designed to extract critical business insights from Walmart sales data. I utilize Python for data processing and analysis, SQL for advanced querying, and structured problem-solving techniques to solve key business questions. The project is ideal for data analysts looking to develop skills in data manipulation, SQL querying, and data pipeline creation.

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Tools Used

• Kaggle Api
• Python (Pandas, SQLAlchemy)
• PostgreSQL
• VS Code

Key Questions Answered

• Busiest day for each branch
• Top-performing product categories
• Profit by category
• Sales by time of day

Data Source

Kaggle Walmart Sales Dataset

What I Learned

• Data cleaning with Pandas
• Loading data into PostgreSQL
• Writing SQL queries with window functions
• Business-focused data analysis

Project Steps

1. Set Up the Environment

• Tools Used: Visual Studio Code (VS Code), Python, SQL (MySQL and PostgreSQL)
• Goal: Create a structured workspace within VS Code and organize project folders for smooth development and data handling.

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.

• 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>

1. Install Python libraries:

pip install -r requirements.txt

1. 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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