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Customer Shopping Behavior Analysis

Project Overview

This project focuses on understanding customer shopping patterns through end-to-end data analytics. The workflow includes exploratory analysis in Python, structured data handling in SQL Server, interactive dashboard creation in Power BI, and a final presentation prepared using Gamma.

Dataset Description

The dataset contains detailed customer transaction records such as purchase dates, product categories, quantities purchased, and pricing information. These attributes support the identification of trends, customer segments, and behavioral insights.

Tools & Technologies Used

Python: Data cleaning, wrangling, and exploratory analytics

SQL Server: Storing, organizing, and querying cleaned datasets

Power BI: Designing dashboards for visualization and business insight exploration

Gamma: Creating a polished project presentation highlighting key outcomes

Project Workflow

Import raw CSV data into Python and perform preprocessing using Pandas.

Load the cleaned dataset into SQL Server tables for structured querying.

Connect SQL Server to Power BI and build visual dashboards for analysis.

Summarize insights and recommendations in a Gamma-based presentation.

Dashboard Highlights

The Power BI dashboard includes metrics such as purchase frequency, average order value, customer categories, and trend patterns. Users can apply interactive filters to analyze data by time period, product category, and customer group.

Key Insights

The analysis uncovers multiple customer segments contributing significantly to overall revenue, reveals seasonal demand patterns, and identifies potential areas for targeted marketing strategies. These insights help in optimizing customer retention and improving business decisions.

How to Use the Project

Download or clone the project repository.

Install required Python packages (pandas, numpy, matplotlib, etc.).

Run preprocessing scripts to generate SQL-compatible output.

Import the processed data into SQL Server using the provided scripts.

Open the Power BI file and connect it to your SQL Server instance.

Navigate through the dashboard and customize visuals as required.

Review the presentation created in Gamma for a summary of results.

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