Welcome to my project on Customer Segmentation!
In this notebook, I applied unsupervised machine learning techniques to identify different types of customers based on their shopping behavior — a key step for businesses to offer personalized experiences.
Customer segmentation is crucial in today's competitive market. By analyzing customer data from a mall, I segmented customers into meaningful groups using the K-Means Clustering algorithm. This helps businesses:
- Target the right audience with the right offers
- Personalize communication
- Optimize product placement and promotions
- 👤 Age
- 💰 Annual Income
- 📈 Spending Score (1–100)
These features provide deep insights into spending patterns and lifestyle behaviors of shoppers.
- Data Preprocessing
- Cleaned the data
- Checked for missing values
- Exploratory Data Analysis (EDA)
- Distribution and scatter plots for understanding data trends
- Elbow Method
- Determined the optimal number of clusters (
k)
- Determined the optimal number of clusters (
- K-Means Clustering
- Applied clustering to segment customers
- Visualization
- 2D and 3D plots to visualize the segmented clusters
- Some customers with high income spend less — indicating potential for targeted campaigns.
- Younger customers often fall into higher spending categories.
- Well-defined clusters highlight unique customer groups based on behavior and income.
- 📄 Mall Customer Dataset
- 📊 200 records of customers
- 🧾 Columns: Customer ID, Gender, Age, Annual Income, Spending Score
This is a popular dataset for clustering tasks and is widely used for learning purposes.
- Scatter plots showing income vs spending
- Clustered data in 2D and 3D space
- Elbow graph showing optimal number of clusters
- Python
- Jupyter Notebook
Libraries Used:
pandasnumpymatplotlibseabornscikit-learn