Overview / Usage

Machine learning has revolutionized the way we approach problems and has opened up new possibilities for solving complex issues. One such problem is the accurate prediction of the price of a diamond by seeing their physical charecteristics.

This project uses the OneDAL library to build and optimize machine learning models for predicting the price of diamonds based on their various characteristics such as carat, cut, color, clarity, depth, and table.

Methodology / Approach

✅ The dataset used in this project is the Diamonds Dataset by ULRIK THYGE PEDERSEN.

✅ It contains 53940 entries of diamonds and their physical charecteristics with price.

✅ Analyse relations between physical charecteristics and price

✅ Used model using intel oneAPI intel oneDAL:The Intel oneAPI Data Analytics Library (oneDAL) contributes to the acceleration of big data analysis by providing highly optimised algorithmic building blocks for all phases of data analytics (preprocessing, transformation, analysis, modelling, validation, and decision making) in batch, online, and distributed processing modes of computation.The library optimizes data ingestion along with algorithmic computation to increase throughput and scalability.

Technologies Used

This project requires the following dependencies:

✅ Python 3.7 or higher

✅ Scikit-learn

✅ Pandas

✅ NumPy

✅ Matplotlib

✅ Seaborn

Repository

https://github.com/arungeekay/Diamond-Price-Prediction-using-OneDAL