Walmart Data Analysis:

This project is data analysis solution designed to extract critical business insights from Walmart sales data.Utilize Python for data processing and analysis, SQL for advanced querying, and structured problem-solving techniques to solve key business questions.

1. Set Up the Environment

• Tools Used: Visual Studio Code (VS Code), Python(pandas, numpy, sqlalchemy, mysql-connector-python, psycopg2), SQL (MySQL and PostgreSQL), Jupyter Notebook
• Data Source: Kaggle’s Walmart Sales Dataset

2. 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.

3. 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.

4. 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.

5. 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.

Results and Insights

This section will include 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.