This project focuses on segmenting customers based on purchasing behavior and demographic attributes to enable data-driven marketing and business decision-making. Python-based analysis and clustering techniques are used to identify distinct customer groups.
- Analyze customer purchase behavior and attributes
- Segment customers into meaningful groups using clustering
- Provide actionable insights for targeted marketing and retention strategies
- Python (Pandas, NumPy)
- Data Visualization (Matplotlib, Seaborn)
- Machine Learning: K-Means Clustering (scikit-learn)
The dataset contains customer-level information such as:
- Purchase Amount (USD)
- Age
- Review Rating
- Previous Purchases
- Product Category
- Season and Location
- Discount Applied (Yes/No)
- Performed data cleaning and preprocessing to handle missing values and ensure data consistency
- Conducted Exploratory Data Analysis (EDA) to understand customer behavior across different features
- Applied feature scaling and used the Elbow Method to identify the optimal number of clusters
- Implemented K-Means clustering to segment customers into distinct groups
- Analyzed cluster-wise characteristics to derive meaningful business insights
- Identified high-value customers with higher spending and frequent purchases
- Recognized price-sensitive customers with higher discount dependency
- Discovered customer segments suitable for personalized marketing strategies
- Supports targeted marketing and promotional campaigns
- Improves customer retention and engagement strategies
- Helps businesses optimize revenue through customer-focused insights
This project demonstrates hands-on experience in customer analytics, clustering techniques, and translating data insights into business value using Python. This project demonstrates hands-on experience in customer analytics, clustering techniques, and translating data insights into business value using Python.