| --- |
| title: UnsupervisedCustumerPrediction |
| emoji: 🧩 |
| colorFrom: indigo |
| colorTo: blue |
| sdk: docker |
| app_port: 8501 |
| tags: |
| - streamlit |
| pinned: false |
| short_description: Streamlit app that predicts cluster labels from uploaded CSV |
| license: mit |
| --- |
| |
| # 🧩 Clustering Predictor (KMeans / GMM) |
|
|
| This Space predicts cluster labels for uploaded tabular data using a saved preprocessing pipeline: |
| - **StandardScaler** |
| - **PCA (95% explained variance)** |
| - A clustering model (**KMeans** or **Gaussian Mixture Model**) |
|
|
| ## ✅ What this app does |
| - Upload a CSV file |
| - The app checks required feature columns |
| - Applies **scaler + PCA** |
| - Outputs **Predicted** cluster label for each row |
| - Lets you download the predictions as a CSV |
|
|
| ## 📦 Required files (must be in the repo root) |
| Place these files next to `app.py`: |
|
|
| - `feature_names.pkl` |
| - `scaler.pkl` |
| - `pca.pkl` |
| - `kmeans_model_k9.pkl` *(optional, if you want KMeans)* |
| - `gmm_model_k9.pkl` *(optional, if you want GMM)* |
|
|
| ## 🧾 Input format |
| Your CSV must include all feature columns stored in `feature_names.pkl`. |
|
|
| Optional: |
| - You may include an `id` or `Id` column. |
| If present, it will be included in the output as `Id`. |
|
|
| ## ▶️ Run locally |
| ```bash |
| pip install -r requirements.txt |
| streamlit run app.py |
| 📝 Notes |
| This is an unsupervised project, so cluster quality is evaluated on Kaggle using the leaderboard score. |
| |
| Visual separation in 2D does not always reflect the Kaggle metric. |
