💎 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.

📊 Performance

RMSE: 605.16
R² Score: 0.9549

🚀 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