This repository contains a machine learning project using XGBoost to predict diamond prices. The project utilizes the popular diamonds dataset (diamonds.csv) to train and evaluate the XGBoost model.
XGBoost is an open-source, scalable machine learning library that provides gradient boosting algorithms. It is widely used for various tasks, including regression, classification, and ranking. In this project, we will use XGBoost for a regression task to predict the prices of diamonds based on their various features.
The dataset used for this project is called diamonds.csv. It contains information about diamonds, including features such as carat weight, cut, color, clarity, depth, table, and price. This dataset is commonly used in machine learning tutorials and competitions and can be found in the data folder.
Download the dataset from the following link and place it in the data folder:
To run the code in this repository, you need to have the following installed:
Python (>= 3.6)
pandas
scikit-learn
XGBoost
You can install the required packages using pip:
pip install pandas scikit-learn xgboost