This project analyzes customer behavior to identify purchasing patterns, customer segments, and key factors influencing customer engagement. The analysis provides actionable insights that support customer retention, targeted marketing, and business growth strategies.
- Understand customer purchasing behavior.
- Identify key customer segments.
- Analyze buying patterns and customer preferences.
- Generate insights to improve customer retention.
- Support data-driven marketing and business decisions.
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Cleaned and validated the dataset.
- Handled missing values and inconsistencies.
- Prepared data for analysis.
- Customer segmentation
- Purchase behavior analysis
- Trend analysis
- Statistical analysis
- Data visualization
- Identified customer behavior patterns.
- Highlighted opportunities for customer retention.
- Provided recommendations for targeted marketing strategies.
- Identified high-value customer segments.
- Analyzed customer purchasing trends and engagement.
- Highlighted opportunities to improve customer retention.
- Generated insights to support personalized marketing campaigns.
This repository contains:
- Jupyter Notebook (.ipynb) – Complete customer behavior analysis.
- Project Report (PDF) – Detailed methodology, analysis, and findings.
- Executive Summary (PDF) – Business-focused summary and recommendations.
Customer_Behaviour_Analysis
│
├── Customer_Behaviour_Analysis.ipynb
├── Customer_Behaviour_Analysis_Report.pdf
├── Executive_Summary_Customer_Behaviour_Analysis.pdf
└── README.md
- Exploratory Data Analysis (EDA)
- Customer Analytics
- Customer Segmentation
- Data Cleaning
- Statistical Analysis
- Data Visualization
- Business Analysis
- Data Storytelling
- Python
Kartikey Singh
Data Analyst | Power BI | Python | SQL | Excel
LinkedIn: Kartikey_Singh
GitHub: Kartikey_Singh