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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# 🛍️ Customer Segmentation using K-Means (Streamlit App)\n\nThis project performs **customer segmentation** using **K-Means Clustering**, and builds an interactive **Streamlit web app** for visual analysis and business insights.\n\nIt helps businesses understand different types of customers like:\n- 🟥 High-income, high-spending customers\n- 🟦 Low-income, low-spending customers\n- 🟪 Impulsive buyers\n- 🟩 Cautious but wealthy customers\n\n---\n\n## 📌 Objective\n\n🎯 To segment customers into useful clusters using machine learning and visualize results dynamically.  \nThe tool allows:\n- Marketing teams to design **targeted strategies**\n- Businesses to **personalize offers**\n- Analysts to **explore patterns** in customer behavior\n## LIVE : https://customer-segmentation-using-kmeans-clustering.streamlit.app/\n\n---\n\n## 🚀 Features\n\n✅ Upload your own dataset  \n✅ Select any 2 numeric features (Age, Income, Spending Score, etc.)  \n✅ Elbow Method to choose the best number of clusters  \n✅ K-Means clustering  \n✅ Dynamic scatter plot with cluster colors  \n✅ Smart report with cluster-wise insights  \n✅ Styled table showing cluster means  \n✅ Business-friendly auto descriptions\n\n---\n\n## 🧪 Technologies Used\n\n| Tool / Library     | Purpose                             |\n|--------------------|--------------------------------------|\n| Python             | Main programming language            |\n| Pandas             | Data analysis \u0026 cleaning             |\n| Matplotlib         | Elbow graph                          |\n| Seaborn            | Cluster visualization                |\n| Scikit-learn       | K-Means clustering                   |\n| Streamlit          | UI for interactive web app           |\n\n---\n\n## 📊 Dataset Overview\n\nWe used `Mall_Customers.csv`, containing:\n\n| Column                | Description                         |\n|------------------------|-------------------------------------|\n| CustomerID            | Unique customer ID                  |\n| Gender                | Male / Female                       |\n| Age                   | Age of customer                     |\n| Annual Income (k$)    | Income in thousands of dollars      |\n| Spending Score (1–100)| Score assigned by the shopping mall |\n\n---\n\n## 🖼️ How It Works\n\n1. Upload the CSV file (or use the default one)\n2. Select 2 numeric features for clustering\n3. See the Elbow Method graph to pick the best number of clusters (K)\n4. Visualize customers in color-coded clusters\n5. Read auto-generated cluster descriptions\n6. View a styled summary table for insights\n\n---\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheanujsinha01%2Fcustomer-segmentation-using-k-means-clustering","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftheanujsinha01%2Fcustomer-segmentation-using-k-means-clustering","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheanujsinha01%2Fcustomer-segmentation-using-k-means-clustering/lists"}