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Diamond Price Prediction

Overview

This project is designed to predict diamond prices using a Machine Learning model. It includes data ingestion, transformation, model training, and prediction. The project is implemented using Python and Flask .

Dataset Link

Diamond Price Dataset

Features

The dataset consists of 10 independent variables (including id):

• id: Unique identifier for each diamond.
• carat: Carat (ct.) is the unit of weight measurement used exclusively for gemstones and diamonds.
• cut: Quality of the diamond cut.
• color: Color of the diamond.
• clarity: A measure of the purity and rarity of the stone, graded by visibility under 10-power magnification.
• depth: Height (in millimeters) measured from the culet (bottom tip) to the table (flat top surface).
• table: The facet of the diamond visible when viewed face up.
• x: Diamond X dimension.
• y: Diamond Y dimension.
• z: Diamond Z dimension.

Target Variable

• price: Price of the given diamond.

Project Structure

DiamondPricePrediction/
├── .dvc
├── .github/workflows/
│           └── main.yaml
├── src/
│   ├── DiamondPricePrediction/
│   │   ├── components/
│   │   │   ├── data_ingestion.py
│   │   │   ├── data_transformation.py
│   │   │   ├── model_evaluation.py
│   │   │   └── model_training.py
│   │   ├── pipelines/
│   │   │     ├── prediction_pipeline.py
│   │   │     └── training_pipeline.py
│   │   ├── utils.py/
│   │   │    └── utils.py
│   │   ├── exception.py
│   │   ├── logger.py
├── templates/
│   ├── index.html
│   ├── predict.html
│   └── result.html
├── app.py
├── Dockerfile
├── dvc.yaml
├── init_setup.sh
├── README.md
├── requirements.txt
├── setup.py
└── template.py

Installation

1. Clone the repository:

   git clone https://github.com/kunalshelke90/DiamondPricePrediction.git
   cd DiamondPricePrediction

2. Create a virtual environment and install dependencies(if you have linux base terminal):

   bash init_setup.sh

   Else

   Create a virtual environment and install dependencies:

   conda create env -p Diamond_Price python=3.8 -y

   conda activate Diamond_Price

   pip install -r requirements.txt

3. Set up environment variables: Create a .env file in the project root and add the following variables:

   DAGSHUB_REPO_OWNER="owner_name"
   DAGSHUB_REPO_NAME="Repo_name"
   DAGSHUB_MLFLOW="True"
   MLFLOW_REGISTRY_URI="https://dagshub.com/repo_owner/repo_name.mlflow"
   
   AWS_ACCESS_KEY_ID=
   AWS_SECRET_ACCESS_KEY=
   

Usage

1. Start the Flask application:

   python app.py

2. Access the application: Open your web browser and navigate to http://localhost:8080 or http://127.0.0.1:8080 to interact with the application.

Using Docker

1. Build the Docker image:

   docker build -t Diamond .

2. Run the Docker container:

   docker run -p 5000:5000 Diamond

Customization

1. Data Ingestion: Customize data_ingestion.py in the src/DiamondPricePrediction/components folder to suit your data source and schema. Modify the connection settings for your Cassandra database and adjust the data loading logic in src/mlproject/utils.py.

2. Data Transformation: Modify data_transformation.py in the src/DiamondPricePrediction/components folder to apply different scaling methods, feature engineering techniques, or transformations according to your dataset's needs.

3. Model Training: Customize model_training.py in the src/DiamondPricePrediction/components folder to experiment with different models, hyperparameters, and evaluation metrics. You can also integrate other ML libraries like TensorFlow or PyTorch.

4. Web Interface: Modify the HTML templates in the templates/ folder to match your preferred UI design. You can add or remove input fields, change styles, and customize the prediction output format.

License

This project is licensed under the MIT License. See the LICENSE file for more details.