README.md

metadata

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

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.