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A newer version of the Gradio SDK is available: 6.25.0
metadata
title: SmartSeg AI
emoji: π€
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.9.1
python_version: 3.11
app_file: app.py
π€ SmartSeg AI β Advanced Customer Segmentation Dashboard
SmartSeg AI is a professional machine learning application that uses K-Means clustering to segment customers based on their demographic and spending behavior. It provides real-time predictions and interactive visualizations to help businesses understand their customer base better.
π Features
- K-Means Clustering: Robust segmentation using industry-standard algorithms.
- Real-time Prediction: Instant classification of new customer data.
- Interactive Dashboards: Visualized using Plotly and Gradio.
- PCA Analysis: 2D projection of high-dimensional data for better cluster visibility.
- Business Insights: Actionable advice based on customer segments.
π Dataset
The project uses the Mall Customer Segmentation dataset, which includes:
- Gender
- Age
- Annual Income (k$)
- Spending Score (1-100)
π οΈ Installation & Local Setup
Clone the repository:
git clone https://huggingface.co/spaces/your-username/smartseg-ai cd smartseg-aiInstall dependencies:
pip install -r requirements.txtTrain the model (Optional):
python train.pyRun the App:
python app.py
π Project Structure
smartseg-ai/
β
βββ data/
β βββ Mall_Customers.csv # Raw dataset
β βββ clustered_customers.csv # Data with cluster labels
βββ model/
β βββ kmeans.pkl # Trained K-Means model
β βββ scaler.pkl # StandardScaler object
β βββ label_encoder.pkl # LabelEncoder for Gender
βββ outputs/
β βββ elbow_plot.png # Elbow method visualization
βββ train.py # Model training script
βββ app.py # Gradio dashboard application
βββ requirements.txt # Python dependencies
βββ README.md # Project documentation
π€ Hugging Face Deployment
The live version of this app is hosted on Hugging Face Spaces.
View the Live Demo: https://huggingface.co/spaces/mallelamanoj75/SmartSeg-AI
How to Deploy Your Own
- Create a new Space with the Gradio SDK.
- Upload all files (including
model/anddata/directories). - The app will automatically build and run.
πΈ Screenshots
(Add screenshots here after running the app)
Built with β€οΈ by Manojkumar