title	emoji	colorFrom	colorTo	sdk	app_port	tags	pinned	short_description	license
UnsupervisedCustumerPrediction	🧩	indigo	blue	docker	8501	streamlit	false	Streamlit app that predicts cluster labels from uploaded CSV	mit

🧩 Clustering Predictor (KMeans / GMM)

🔗 Live Demo:(https://huggingface.co/spaces/EnYa32/UnsupervisedCustumerPrediction)

📓 Kaggle Competition: https://www.kaggle.com/code/enesyama/unsupervisedcustumer-clustering

⚡ Quick Facts

• Task: Unsupervised Clustering
• Models: KMeans + Gaussian Mixture Model
• Preprocessing: StandardScaler + PCA (95%)
• Input: Tabular CSV
• Output: Cluster Labels
• Deployment: Streamlit App
• Pipeline: Saved sklearn objects (.pkl)

🧠 Pipeline Structure

Raw Features
→ StandardScaler
→ PCA (95% variance)
→ Clustering Model (KMeans / GMM)
→ Cluster Label Prediction

✅ 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

📸 Model & Clustering Visualizations

PCA Explained Variance

KMeans Elbow Method

Cluster Visualization (PCA 2D)

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📊 Evaluation

Because this is an unsupervised learning project, evaluation is not based on labeled accuracy.

Model quality was evaluated using:

• Kaggle leaderboard score
• Cluster stability
• PCA visualization
• Distribution consistency across clusters

🛠️ Tech Stack

• Python
• scikit-learn
• PCA
• KMeans
• Gaussian Mixture Models
• Streamlit
• Pickle model persistence

📦 Required Model Artifacts

The following trained pipeline artifacts must exist in the repo root:

• feature_names.pkl
• scaler.pkl
• pca.pkl
• kmeans_model_k9.pkl
• gmm_model_k9.pkl

🧾 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


Visual separation in 2D does not always reflect the Kaggle metric.

You can download the CSV file here:
https://www.kaggle.com/competitions/tabular-playground-series-jul-2022/data