Python & sql project
Exploration Data Analysis (EDA) & Answering Business Questions
The project involved conducting a comprehensive business analysis of Walmart’s operations using a dataset containing sales and performance data. The goal was to uncover actionable insights that could help Walmart optimize its operations, improve customer satisfaction, and drive revenue growth.
Project Objectives:
1- Understand Customer Behavior.
2- Enhance Operational Efficiency.
3-Maximize Profitability.
4-Support Strategic Decisions.
The project followed a structured approach to clean, transform, analyze, and derive actionable insights from Walmart’s dataset. The process can be categorized into three main phases: Data Preparation, Data Analysis, and Insights Delivery.
Data Preparation
This phase focused on organizing the raw dataset into a usable format for analysis.
Steps:
Data Cleaning and Transformation (Python):
Loaded the dataset and handled inconsistencies and missing values.
Exported the cleaned dataset for further analysis.
Data Loading into SQL:
Connected Python to a SQL database.
Loaded the cleaned dataset into SQL tables for structured querying and analysis.
Data Analysis
Conducted SQL queries to answer critical business questions, such as
• identifying top-performing categories,
• analyzing payment methods,
• evaluating branch performance.
Performed profitability analysis by calculating metrics such as
• total profit,
• average ratings,
• revenue trends.
Insights Delivery
• Identified trends in customer behavior, including preferred payment methods and highly rated product categories.
• Highlighted operational patterns, such as the busiest days and shifts across branches.
• Ranked product categories and branches based on profitability and performance metrics.
• Addressed revenue decreases by identifying underperforming branches and providing recommendations for improvement.
Explore the scripts and queries used for data cleaning, transformation, and analysis.
Dataset URL 🔗
Python Notebook
SQL Queries