Synent Technologies Internship | Task 6- Customer Segmentation using K-Means | Mall Customer Dataset

Data Analyst Internship Submission — Synent Technologies Task 6: Customer Segmentation 👤 Name: Vibhu 🎯 Objective: Group mall customers into distinct behavioral segments using K-Means Clustering based on annual income and spending score. 📊 Key Insights: Optimal number of clusters determined using the Elbow Method: K = 5 5 distinct customer segments identified: Premium Customers, Conservative Savers, Impulsive Buyers, Standard Customers, Budget Conscious No customer above age 40 has a spending score higher than 60 — premium marketing should target the under-40 demographic Standard Customers form the largest segment (80+ customers), serving as the baseline revenue driver 🛠️ Tools Used: Python, Pandas, Scikit-learn, Matplotlib, Seaborn 🔗 GitHub Repository: [paste your repo link here] 📂 Dataset: Mall Customer Segmentation Dataset (Kaggle) #MachineLearning #KMeansClustering #CustomerSegmentation #Python #Internship #SynentTechnologies