💎 Diamond (Gemstone) Price Prediction

This project aims to accurately predict the price of gemstones (diamonds) using machine learning techniques. It follows a modular, production-grade ML pipeline structure, including best practices like logging, exception handling, and environment management using conda. The goal is to build a clean, scalable, and deployable price prediction system.

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📂 Project Structure

Diamond-Price-Prediction/
│
├── notebooks/              # Jupyter Notebooks for EDA and experimentation
│   ├── data/
│   │   └── gemstone.csv    # Dataset used for prediction
│   ├── EDA.ipynb           # Exploratory Data Analysis
│   └── Model Training.ipynb# Model building in notebook
│
├── src/                    # Source code package
│   ├── components/         # Core ML components
│   │   ├── data_ingestion.py
│   │   ├── data_transformation.py
│   │   └── model_trainer.py
│   ├── pipelines/          # Training & prediction pipelines
│   ├── exception.py        # Custom exception handling
│   ├── logger.py           # Logging
│   └── utils.py            # Utility functions
│
├── templates/              # HTML templates (optional for frontend)
├── app.py                  # Entry point for running the app (Flask/FastAPI)
├── requirements.txt        # Python dependencies
├── setup.py                # Installation script for the project
├── Readme.md               # Project documentation
└── .gitignore              # Ignored files in version control

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✅ Features

• 🔍 End-to-End ML pipeline: Covers data ingestion, transformation, model training, and prediction.
• 🔒 Robust Exception Handling: Clean error tracking using custom exception classes.
• 🧾 Detailed Logging: Easily track the model flow and issues using logs.
• 🔧 Modular Codebase: All ML components are separated into modules for reusability and maintainability.
• 🧪 Jupyter Notebook Support: For data exploration and experimentation.
• 📦 Production-ready Packaging: Easily deploy or distribute the project via setup.py.
• 📁 Conda Environment Support: Isolated, reproducible environment setup.
• 🔬 Pipeline Integration: Training and prediction steps are structured in automated pipelines.

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🧠 Model Training Workflow

• 📥 Data Loading: Loaded the gemstone.csv dataset using pandas.
• 🧹 Data Cleaning: Dropped unnecessary columns (e.g., id) to prepare the dataset.
• 🔄 Preprocessing: Applied data transformation (likely includes encoding, scaling — further confirmed in next cells).
• ✂️ Feature Engineering: Selected relevant features for modeling.
• 🧪 Train-Test Split: Divided data into training and testing sets to evaluate model generalization.
• 📈 Model Building: Trained regression models (such as Linear Regression, Random Forest, or others).
• ✅ Model Evaluation: Evaluated model performance using metrics like R² Score, MAE, or RMSE.
• 💾 Model Persistence: Saved the trained model for future inference or deployment using pickle or joblib.

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**

🧩 Workflow Diagram

Flow of your SoulCare system:

🚀 How to Use

1. Clone the Repository

git clone https://github.com/your-username/Diamond-Price-Prediction.git
cd Diamond-Price-Prediction

2. Setup Environment

With Conda:

conda create -n gemstone_env python=3.9 -y
conda activate gemstone_env
pip install -r requirements.txt

3. Prepare Data

Ensure your dataset (gemstone.csv) is placed inside notebooks/data/.

4. Run Notebooks

To explore the data and initial experiments:

jupyter notebook
# Open EDA.ipynb or Model Training.ipynb

5. Run Training Pipeline

python src/pipelines/training_pipeline.py

6. Start Prediction / App Interface

python app.py

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🔮 Future Scope

• ✅ Integrate a frontend UI using Flask/Streamlit for user interaction.
• 🗃 Add model versioning using MLflow or DVC.
• 🧠 Experiment with advanced models (XGBoost, CatBoost).
• 📊 Add evaluation dashboard using Streamlit or Dash.
• ☁ Deploy on cloud (AWS/GCP/Azure) with Docker & CI/CD.
• 🔍 Add hyperparameter tuning using Optuna or GridSearchCV.
• 📱 Develop a mobile-friendly interface or API.

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🔍 Requirements

Install all the packages using:

pip install -r requirements.txt

Common dependencies:

• numpy
• pandas
• scikit-learn
• matplotlib
• seaborn
• jupyter
• Flask (or FastAPI)

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✍️ Author

Mahesh Singh AI & Data Science Enthusiast LinkedIn | GitHub

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📜 License

This project is licensed under the MIT License.