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README.md

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 ---
+title: "💎 Gemstone Price Regression"
+emoji: 💰
+colorFrom: indigo
+colorTo: blue
+sdk: streamlit
+app_file: app.py
+pinned: true
 license: mit
+tags:
+  - regression
+  - machine-learning
+  - streamlit
+  - diamonds
+  - synthetic-data
 ---
+
+# 💎 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
+
+```bash
+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
+

model_columns.pkl

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rf_model.pkl

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