This project is an end-to-end data analysis solution designed to extract critical business insights from Walmart sales data. We 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.
- 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.
- 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 to pull datasets directly into your project.
- 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.
- Install Required Libraries and Load Data
. Libraries: Install necessary Python libraries using:
. pip install pandas numpy sqlalchemy mysql-connector-python
. Loading Data: Read the data into a Pandas DataFrame for initial analysis and transformations.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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.