Table of Contents

• Overview
• Data Sources
• Extract, Transform, Load
• Exploratory Data Analysis
• Data Visualization
• Conclusion

Overview

This project leverages PostgreSQL, Python, and Tableau to deliver comprehensive retail data insights. The workflow includes:

• ETL (Extract, Transform, Load): Data is ingested and transformed using Python scripts, then loaded into a PostgreSQL database for structured storage and efficient querying.
• Exploratory Data Analysis (EDA): SQL queries in PostgreSQL are used to explore, summarize, and analyze the dataset, uncovering trends and patterns.
• Data Visualization: A Tableau dashboard is created to visualize key findings, enabling interactive exploration and clear communication of sales insights.

Data Sources

Kaggle - Walmart Dataset

The Walmart Dataset contains historical sales data from 2010-02-05 to 2012-11-01 in the file Walmart.csv for 45 Walmart stores located in different regions. The dataset is designed for time series forecasting and retail analytics.

Historical coverage: Sales

Fields in this file include:

• Store: Store number
• Date: Week of sales
• Weekly_Sales: Sales for the given store
• Holiday_Flag: Whether the week is a special holiday week (1 – Holiday week, 0 – Non-holiday week)
• Temperature: Temperature on the day of sale
• Fuel_Price: Cost of fuel in the region
• CPI: Prevailing consumer price index
• Unemployment: Prevailing unemployment rate

Holiday Events:
Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13
Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13
Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13
Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

ETL (Extract, Transform, Load)

The ETL process is implemented using a Python script to extract data from the source CSV file, transform the data into a suitable format, and load it into a PostgreSQL database.

The following steps are performed:

• Data extraction from the Walmart.csv file using Pandas.
• Data transformation to convert data types.
• Loading the transformed data into PostgreSQL using SQLAlchemy.

Exploratory Data Analysis (EDA)

EDA is performed using SQL queries in PostgreSQL to analyze the dataset, uncovering trends and patterns. The analysis queries are saved as database views for reusability and efficient access.

Results from these views are exported as CSV files, which are then imported into Tableau Public for visualization and further exploration.

Key insights include:

• Total sales per store
• Average weekly sales
• Sales trends over time
• Impact of holidays on sales

Conclusion

The project successfully demonstrates the use of PostgreSQL, Python, and Tableau to extract, transform, load, analyze, and visualize retail data. Insights gained from the analysis can help in making informed business decisions and improving sales strategies.

The ETL process ensures that the data is clean and structured, making it easier to analyze and visualize. The Tableau dashboard provides an interactive way to explore the data and gain insights into sales trends, seasonal patterns, and the impact of holidays on sales. Overall, this project showcases the power of data analytics in the retail industry and its potential to drive business growth.

The project can be extended by incorporating additional data sources, such as customer demographics or product information, to gain deeper insights into sales performance. Additionally, machine learning techniques can be applied to predict future sales trends and optimize inventory management.