🛒 Customer Behaviour Analysis

Complete end-to-end data analytics project demonstrating the full workflow from data cleaning to insights using Python, SQL, and Power BI.

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📊 Project Overview

This project analyzes customer shopping behavior to uncover patterns, trends, and insights that drive business decisions. It combines data engineering, exploratory analysis, and interactive visualization to tell a complete data story.

Key Value: Transforms raw customer data into actionable business insights through rigorous analysis and compelling visualizations.

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🎯 Project Objectives

• Understand Customer Behavior: Identify purchasing patterns across demographics and categories
• Segment Analysis: Discover distinct customer segments and their characteristics
• Trend Identification: Uncover seasonal and temporal trends in customer activity
• Business Insights: Provide data-driven recommendations for marketing and sales strategies

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📁 Project Structure

Customer_Behaviour_Analysis/
├── customer_shopping_behavior.csv    # Raw data source
├── data_cleaning.py                  # Data preprocessing
├── customer_behaviour_analysis.ipynb # Complete analysis notebook
├── customer_behaviour_queries.sql    # SQL queries
├── customer_behaviour_dashboard.pbix # Power BI dashboard
└── README.md                         # Documentation

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🛠️ Tools & Technologies

Data Processing

• Python: Pandas, NumPy, Matplotlib, Seaborn
• Data Cleaning: Handling missing values, outliers, data type conversions
• Exploratory Data Analysis (EDA): Statistical analysis and visualization

Database

• SQL: MySQL for querying and data aggregation
• Query Optimization: Efficient data retrieval and transformation

Business Intelligence

• Power BI: Interactive dashboards and reports
• Data Visualization: Creating compelling visual narratives

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📈 Key Findings

Customer Demographics

• Total Customers Analyzed: 3.9K
• Gender Distribution: Female: 1.248K Male: 2.652K

Purchase Patterns

• Most Popular Product Categories: Clothing
• Average Purchase Value: $59.76

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🔧 How to Use This Project

Prerequisites

pip install pandas numpy matplotlib seaborn jupyter

Step 1: Data Preparation

python data_cleaning.py

Step 2: Exploratory Analysis

jupyter notebook customer_behaviour_analysis.ipynb

Step 3: SQL Analysis

Run queries in customer_behaviour_queries.sql

Step 4: Visualization Dashboard

Open customer_behaviour_dashboard.pbix in Power BI Desktop

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📊 Dashboard Features

✅ Customer Overview: Total customers, average age, gender distribution ✅ Purchase Analytics: Transaction volume, average order value ✅ Seasonal Trends: Month-over-month analysis ✅ Segment Performance: Metrics across customer segments

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🚀 Skills Demonstrated

• Data Analysis & Statistical Analysis
• SQL Query Optimization
• Python Data Manipulation
• Data Visualization & Storytelling
• Business Intelligence

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📞 Contact & Collaboration

Interested in collaborating? Feel free to reach out on LinkedIn

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Last Updated: January 11, 2026 Author: Charles Maina Irungu | GitHub | LinkedIn