The Diamond Price Prediction Web App is a user-friendly and interactive web application designed to predict the price of diamonds based on various attributes. This project demonstrates the integration of a machine learning model with a web interface to provide a seamless experience for users interested in estimating the value of their diamonds.
- Input Form: Users can input various attributes of a diamond, including carat, depth, table, dimensions (x, y, z), cut, color, and clarity.
- Machine Learning Prediction: The application uses a trained machine learning model to predict the price of the diamond based on the provided attributes.
- User-Friendly Interface: The web app features an attractive and intuitive interface, making it easy for users to enter data and receive predictions.
- Background Image: The app uses a captivating background image to enhance the visual appeal and engagement of users.
The dataset used in the Diamond Price Prediction Web App project is a collection of diamond attributes and their corresponding prices. The dataset is utilized to train a machine learning model that can predict the price of a diamond based on its various characteristics. The dataset provides a valuable resource for understanding the relationships between diamond attributes and their market values.
carat: Carat (ct.) refers to the unique unit of weight measurement used exclusively to weigh gemstones and diamonds.cut: Quality of Diamond Cut.color: Color of Diamond.clarity: Diamond clarity is a measure of the purity and rarity of the stone, graded by the visibility of these characteristics under 10-power magnification.depth: The depth of the diamond is its height (in millimeters) measured from the culet (bottom tip) to the table (flat, top surface).table: A diamond's table is the facet which can be seen when the stone is viewed face up.x: Diamond X dimension.y: Diamond Y dimension.z: Diamond Z dimension.
- Front-End: HTML, CSS
- Back-End: Python (Flask framework)
- MachineLearning: Linear Regression, Lasso Regression, Ridge Regression, Decision Tree
git clone https://github.com/ayushgoyal004/DiamondPricePredictor
conda create -p venv python==3.8 conda activate venv/
pip install -r requirements.txt
python application.py
- Visit the web app. :- http://127.0.0.1:5000/
- Enter the attributes of the diamond in the input form.
- Click the "Predict" button.
- Receive the predicted price of the diamond.
Contributions to this project are welcome! If you have ideas for improvement, bug fixes, or additional features, feel free to create a pull request or open an issue.