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---
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title: "💎 Gemstone Price Regression"
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emoji: 💰
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colorFrom: indigo
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colorTo: blue
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sdk: streamlit
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app_file: app.py
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pinned: true
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license: mit
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tags:
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- regression
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- machine-learning
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- streamlit
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- diamonds
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- synthetic-data
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---
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# 💎 Gemstone Price Prediction App
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This Streamlit app predicts the price of a gemstone using its physical and quality-related features.
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## 🧠 Project Overview
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- This project simulates a **gemstone pricing system** using synthetic tabular data.
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- Features include: `carat`, `depth`, `table`, `x`, `y`, `z`, `clarity_score`, `color_score`, and `cut_score`.
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- The target variable is **price** (USD).
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- Model: **RandomForestRegressor**
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- Trained on 1000 synthetic samples.
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---
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## 📊 Performance
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- RMSE: **605.16**
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- R² Score: **0.9549**
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---
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## 🚀 How to Run Locally
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```bash
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pip install -r requirements.txt
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streamlit run app.py
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🔮 Future Work
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Area Improvement
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Model Try XGBoost, LightGBM
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Feature Engineering Interaction terms, log/carat scaling
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Deployment Add API endpoint with FastAPI
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Real-world Data Integrate real gemstone datasets
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📁 Files
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app.py: Streamlit interface
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rf_model.pkl: Trained model
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model_columns.pkl: List of input features
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requirements.txt: Required libraries
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