Customer Segmentation Analysis Using K-Means Clustering | Oasis Infobyte Internship Task 2

📊 Customer Segmentation Analysis Using K-Means Clustering Hello everyone, This project was completed as part of the Oasis Infobyte Data Analytics Internship Program. 👨‍💻 Name: Abenet Asnake Tesfaye 📚 Domain: Data Analytics 📌 Level 1 - Task 2 🔹 Project Objective The objective of this project is to analyze customer data and segment customers into different groups based on their Annual Income and Spending Score using the K-Means Clustering algorithm. 🔹 Tools and Technologies Used ✅ Python ✅ Pandas ✅ NumPy ✅ Matplotlib ✅ Seaborn ✅ Scikit-Learn ✅ Google Colab 🔹 Project Workflow • Data Loading and Cleaning • Exploratory Data Analysis • Customer Demographics Analysis • Elbow Method • K-Means Clustering • Customer Segmentation Visualization • Business Recommendations 🔹 Key Findings ✅ Five customer segments were identified ✅ High-income high-spending customers were identified as premium customers ✅ Different customer groups require different marketing strategies ✅ Customer segmentation helps improve targeted marketing and customer retention 🔹 Business Recommendations • Focus on premium customers through loyalty programs • Develop personalized marketing campaigns • Improve customer engagement strategies • Use segmentation for better decision-making 📂 GitHub Repository: https://github.com/abenet-asnake/OIBSIP/tr... 🎥 Internship Project Video #DataAnalytics #MachineLearning #CustomerSegmentation #KMeansClustering #Python #DataScience #OasisInfobyte #GoogleColab #InternshipProject #ScikitLearn