This project performs customer segmentation using machine learning to group customers based on their behavior, demographics, or purchasing patterns. Segmentation helps businesses tailor marketing strategies, improve customer retention, and optimize sales strategies.
Clustering Algorithm: K-Means
Source: Mall Customers from Kaggle
Demographics (Age, Gender,)
Behavioral Data (Spending Score)
Transactional Data (Annual Income)
Data Cleaning
Handling missing values, outliers
Feature scaling (StandardScaler, MinMaxScaler)
Exploratory Data Analysis (EDA)
Visualizing distributions, correlations
RFM scoring (if applicable)
Clustering
Optimal cluster selection (Elbow Method)
Model training & evaluation
Visualization
2D/3D plots of clusters (Matplotlib, Plotly, Seaborn)
Business insights per segment
Deployment in progress
Targeted Marketing: Custom promotions for high-value customers
Customer Retention: Identify at-risk segments
Inventory Management: Stock products preferred by key segments
Python (Pandas, NumPy, Scikit-learn)
Visualization: Matplotlib, Seaborn