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https://github.com/kunalshelke90/diamondpriceprediction
This project focuses on developing a machine learning model to predict the price of diamonds based on various attributes. By analyzing a dataset that includes information about the carat weight, cut, color, clarity, and other factors, we aim to create a model that can accurately estimate the price of diamonds
https://github.com/kunalshelke90/diamondpriceprediction
aws dagshub eda feature-engineering flask githubactions machine-learning mlflow python
Last synced: 6 days ago
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This project focuses on developing a machine learning model to predict the price of diamonds based on various attributes. By analyzing a dataset that includes information about the carat weight, cut, color, clarity, and other factors, we aim to create a model that can accurately estimate the price of diamonds
- Host: GitHub
- URL: https://github.com/kunalshelke90/diamondpriceprediction
- Owner: kunalshelke90
- License: mit
- Created: 2024-01-02T11:04:19.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2024-10-24T12:20:27.000Z (22 days ago)
- Last Synced: 2024-10-25T12:42:26.195Z (21 days ago)
- Topics: aws, dagshub, eda, feature-engineering, flask, githubactions, machine-learning, mlflow, python
- Language: Jupyter Notebook
- Homepage:
- Size: 10.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
---
# 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](https://www.kaggle.com/competitions/playground-series-s3e8/data)
### 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
```bash
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**:
```bash
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
bash init_setup.sh
```
# Else
**Create a virtual environment and install dependencies**:
```bash
conda create env -p Diamond_Price python=3.8 -y
```
```bash
conda activate Diamond_Price
```
```bash
pip install -r requirements.txt
```
3. **Set up environment variables**:
Create a `.env` file in the project root and add the following variables:
```plaintext
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**:
```bash
python app.py
```
2. **Access the application**:
Open your web browser and navigate to [http://localhost:8080](http://localhost:8080) or [http://127.0.0.1:8080](http://127.0.0.1:8080) to interact with the application.
## Using Docker
1. **Build the Docker image**:
```bash
docker build -t Diamond .
```
2. **Run the Docker container**:
```bash
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](LICENSE) file for more details.
---