🧾 Mall Customer Segmentation using K-Means Clustering 🎯 Objective
To group mall customers into distinct segments based on:
Annual Income (k$)
Spending Score (1–100) so businesses can target each segment with personalized marketing.
Below are the key visual insights from the dataset, represented using six compact side-by-side figures.
📸 Visual Summary of Findings
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🔍 What the Results Reveal
Gender Distribution shows a slightly larger female customer base (56%) compared to male (44%).
Boxplots reflect income variance across clusters—clear separation of low, mid, and high-income groups.
Spending Score vs Annual Income scatter plot reveals distinct clusters of customer behavior.
Elbow Method confirms the optimal number of clusters as k = 5.
Histograms show underlying distribution of age, income, and spending diversity in the dataset.
🧠 Algorithm Used
K-Means Clustering (Unsupervised Learning)
Finds natural groupings in data without predefined labels.
Optimal number of clusters determined using Elbow Method (k = 5).
⚙️ Tech Stack
Category Tools Language Python Libraries pandas, numpy, matplotlib, seaborn, scikit-learn Algorithm K-Means Clustering 📊 Steps Involved
Data Collection → Mall_Customers.csv
Exploratory Data Analysis (EDA) → Age, Income, Spending distributions
Feature Selection → Annual Income & Spending Score
Elbow Method → Found optimal k = 5
Clustering → Applied K-Means algorithm
Visualization → Scatter plots & boxplots of clusters
🧩 Cluster Insights
Cluster Income Level Spending Behavior Strategy 0 Low High Budget offers 1 Medium Medium Retain via loyalty programs 2 High High VIP / Premium marketing 3 Low Low Low engagement 4 High Low Upselling & awareness campaigns 📈 Results
5 unique customer segments discovered.
Clear distinction between income and spending patterns.
Useful for targeted marketing and customer retention.
🧑💻 Project By
🌐 github.com/pun33th45