The purpose of this repository is as a tutorial for creating a regression model that predicts the price of a diamond, and building a simple frontend application to make the model publicly available using Streamlit.
The data we will be using for this project is the Diamonds dataset, which is publicly available via Kaggle. It contains 53940 observations, and 10 features in the dataset.
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Pandas for accessing the data, and preparing the it for modeling.
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XGBoost for creating a gradient-boosted regression model.
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Streamlit for creating a frontend application