Unlock actionable customer insights from the Mall Customers dataset using machine learning. Segment customers by behaviour, identify patterns, and drive targeted marketing strategies — all in one premium dashboard.
A complete end-to-end customer analytics pipeline — from raw data to actionable insights.
Comprehensive step-by-step EDA with shape, dtypes, null checks, outliers, distributions, and correlations.
Remove duplicates, handle missing values, encode categoricals, and validate data types for a clean feature set.
Apply StandardScaler + K-Means with Elbow Method and Silhouette Score to find the optimal number of segments.
Plotly-powered histograms, scatter plots, heatmaps, cluster plots, and radar charts — all fully interactive.
Profile each customer segment with income, spending, age, and gender patterns for business decision-making.
Practical, cluster-specific marketing strategies — loyalty programmes, targeted discounts, VIP offers, and more.
Enter any customer's data and get their predicted segment instantly, with results saved to a SQLite database.
Monitor cluster health, customer distribution, and key segmentation metrics from one analytics view.
The entire machine learning workflow is implemented transparently — every step explained with code, theory, and visualisations.
Start with EDALoad Mall_Customers.csv, check shape, dtypes, and missing values.
Distributions, outliers, correlations, and gender analysis.
Remove duplicates, encode gender, drop CustomerID.
StandardScaler on Annual Income and Spending Score.
Determine optimal K using WCSS and silhouette scores.
Fit final model with K=5, assign cluster labels.
Profile clusters and generate marketing strategies.
Dive into the full analytics pipeline or try predicting a new customer's segment right now.