handecarkci/Scrabble-Player-Rating

💎 Gemstone Price Prediction App

This Streamlit app predicts the price of a gemstone using its physical and quality-related features.

🧠 Project Overview

• This project simulates a gemstone pricing system using synthetic tabular data.
• Features include: carat, depth, table, x, y, z, clarity_score, color_score, and cut_score.
• The target variable is price (USD).
• Model: RandomForestRegressor
• Trained on 1000 synthetic samples.

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📊 Performance

• RMSE: 605.16
• R² Score: 0.9549

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🚀 How to Run Locally

pip install -r requirements.txt
streamlit run app.py



🔮 Future Work
Area	Improvement
Model	Try XGBoost, LightGBM
Feature Engineering	Interaction terms, log/carat scaling
Deployment	Add API endpoint with FastAPI
Real-world Data	Integrate real gemstone datasets


📁 Files
app.py: Streamlit interface

rf_model.pkl: Trained model

model_columns.pkl: List of input features

requirements.txt: Required libraries