👨🏻‍💻 Customer Behavior Data Analyst Portfolio Project

End-to-End Data Analytics Workflow: Python | SQL | Power BI This repository contains a comprehensive, industry-standard data analytics project that simulates the full workflow of a professional Data Analyst. The project transitions from raw data processing to high-level strategic business intelligence.

Note: This project was developed following the guided implementation by Amlan Mohanty. It serves as a practical application of data engineering and visualization techniques in a retail business context.

📌 Project Objective

The goal is to analyze retail customer shopping behavior to extract actionable insights. By processing raw datasets, I aimed to identify customer segments, loyalty patterns, and key purchase drivers to help stakeholders make data-driven marketing and inventory decisions.

🛠️ Tech Stack & Workflow

The project is divided into four critical stages:

1. Data Preparation & EDA (Python):

   • Cleaned and transformed raw datasets using Pandas and NumPy.

   • Performed Exploratory Data Analysis (EDA) to identify outliers and missing values.

2. Database Management (SQL):

   • Migrated cleaned data from Python to a SQL Server.

   • Wrote complex queries to answer specific business questions regarding customer retention and high-value segments.

3. Data Visualization (Power BI):

   • Connected the SQL database to Power BI.

   • Developed an interactive dashboard to track KPIs such as average spend, purchase frequency, and demographic trends.

4. Reporting & Strategy:

   • Synthesized technical findings into a business report with actionable recommendations.

📊 Key Features & Business Questions Answered

• Customer Segmentation: Which age groups and genders contribute most to total revenue?

• Loyalty Analysis: How do subscription statuses impact purchase frequency?

• Product Insights: What are the top-performing product categories across different seasons?

• Payment & Shipping Trends: Identifying the most preferred transaction methods to optimize checkout experiences.

📂 Repository Structure

The repository is organized to follow the logical progression of a data project:

• customer_shopping_behavior.csv: The raw retail dataset used for the analysis.

• customer_shopping_behavior.ipynb: Python notebook for data import, exploration, cleaning, and SQL database connection.

• customer behaviour.sql: SQL scripts containing business-logic queries to extract insights.

• Business Problem.pdf: Document outlining the core objectives and questions this project aims to solve.

• Customer Shopping Behavior Report Analysis.pdf: A formal project report summarizing key findings and business recommendations.

• Customer Shopping Behavior Analysis.pptx: A presentation deck designed to communicate insights to stakeholders.

📜 Credits & Reference

This project was implemented as part of a technical portfolio build.

Original Project Creator: Amlan Mohanty

Tutorial Link: Advanced Data Analysis Portfolio Project