Walmart_sales_Analysis_using_SQL_Python

End-to-end SQL project analyzing Walmart sales data using PostgreSQL. Includes data cleaning with Python(pandas), business insights, and advanced SQL queries to solve real-world retail problems

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

Project Overview

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 and SQL querying,\

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Project Steps

1. Set Up the Environment

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

2. Download Walmart Sales Data

• Data Source: Used the Kaggle to download the Walmart sales datasets.
• Dataset Link: Walmart Sales Dataset

3. Install Required Libraries and Load Data

• Libraries: Installd necessary Python libraries using:

  pip install pandas numpy sqlalchemy psycopg2

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

4. Explore the Data

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

5. 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.

6. Feature Engineering

• Create New Columns: Calculated the Total_price 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.

7. Load Data into PostgreSQL

• Set Up Connections: Connect to PostgreSQL using sqlalchemy and load the cleaned data into each database.
• Table Creation: Set up tables in 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.

8. 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.

9. Project Publishing and Documentation

• Documentation: Maintain well-structured documentation of the entire process in Markdown or a Jupyter Notebook.
• Project Publishing: Published the completed project on GitHub:
  • The README.md file (this document).
  • Jupyter Notebooks.
  • SQL query scripts.
  • Data files or steps to access them.

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Requirements

• Python 3.8+
• SQL Databases: MySQL, PostgreSQL
• Python Libraries:
  • pandas, numpy, sqlalchemy , psycopg2

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

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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.

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Auther

Akash Kumar Gupta