Customer Shopping Behavior Analysis

Project Overview

This project analyzes customer shopping behavior to identify purchasing trends, spending patterns, and customer demographics. Using Python and Power BI, the dataset is cleaned, analyzed, and visualized to generate meaningful business insights.

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Objectives

• Understand customer purchasing behavior.
• Analyze spending habits across different customer groups.
• Identify trends in shopping preferences.
• Create interactive dashboards for business decision-making.
• Generate actionable insights from customer data.

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Dataset

The dataset contains customer shopping information, including:

• Customer demographics
• Purchase amounts
• Product categories
• Shopping frequency
• Customer preferences
• Transaction details

File:

customer_shopping_behavior.csv

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Tools & Technologies

Python

• Pandas
• NumPy
• Jupyter Notebook

Business Intelligence

• Power BI

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Project Files

Customer-Shopping-Behavior-Analysis/
│
├── customer_shopping_behavior.csv
├── Custmer_shopping_behaviour.ipynb
├── customer_behavior_dashboard.pbix
├── Customer Shopping Behavior Analysis.docx
└── README.md

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Project Workflow

1. Data Collection

The customer shopping dataset is imported and examined for structure and quality.

2. Data Cleaning

• Missing value treatment
• Data validation
• Data type conversion
• Duplicate removal

3. Exploratory Data Analysis (EDA)

Analysis performed on:

• Customer demographics
• Purchase behavior
• Spending patterns
• Product preferences
• Shopping trends

4. Dashboard Creation

Power BI dashboard developed to visualize:

• Customer distribution
• Purchase trends
• Revenue insights
• Category performance
• Customer segments

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Key Analysis Areas

Customer Demographics

Understanding customer age groups, gender distribution, and shopping patterns.

Purchase Behavior

Analyzing purchase frequency and spending habits.

Product Preferences

Identifying the most popular product categories.

Revenue Insights

Understanding revenue contribution from different customer segments.

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Power BI Dashboard

The Power BI dashboard provides:

• Interactive filtering
• Customer segmentation
• Spending analysis
• Trend visualization
• Business insights

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Screenshots

Add screenshots of:

• Power BI Dashboard Overview

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Business Insights

Some potential insights include:

• High-value customer segments
• Popular product categories
• Seasonal shopping trends
• Customer spending patterns
• Revenue-driving demographics

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Future Improvements

• Predictive customer analytics
• Customer segmentation using machine learning
• Sales forecasting
• Recommendation systems
• Customer lifetime value analysis

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Author

Samriti

Data Analytics | Python | Power BI | Business Intelligence