The Diamond Price Prediction project uses advanced machine learning techniques to predict the price of diamonds based on their physical and quality attributes. This application provides real-time predictions through a Flask web application, making it a valuable tool for buyers, sellers, and industry professionals.
- Launch the Flask web application.
- Input the diamond attributes such as carat, cut, color, clarity, depth, table, x, y, and z.
- Click the "Predict" button to get the diamond price instantly.
- Predicts diamond prices with high accuracy (97%) using regression models.
- Supports key features like carat weight, cut quality, color grade, clarity, and dimensions.
- Interactive and user-friendly web interface built with Flask.
- Visualizes feature relationships with diamond prices using Matplotlib and Seaborn.
- Python: Core programming language for development.
- Flask: Framework for building the web application.
- Pandas & Numpy: For data manipulation and preprocessing.
- Scikit-learn: For implementing regression algorithms.
- Matplotlib & Seaborn: For data visualization.
- Machine Learning Models:
- Linear Regression
- Decision Tree
- Random Forest
- Gradient Boosting
The dataset contains various diamond attributes such as carat weight, cut, color, clarity, depth, table, dimensions (x, y, z), and price. It is preprocessed for training and testing the machine learning models.
- Add support for more diamond attributes to improve prediction accuracy.
- Implement a mobile-friendly interface for broader accessibility.
- Provide exportable reports of predicted prices for users.
- Integrate real-time market data for dynamic price adjustments.
Happy Predicting! 💎