Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Steel Plates Faults dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: This dataset contains over 30,000 cases of liver disease diagnosis results. The researcher trained machine learning models using this dataset to test the feasibility of applying machine learning techniques for making diagnostic predictions.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 86.97%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 99.87%.

CONCLUSION: In this iteration, the XGBoost model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Liver Disease Patients Dataset

Dataset ML Model: Binary-Class classification with numerical and categorical features

Dataset Reference: https://www.kaggle.com/datasets/abhi8923shriv/liver-disease-patient-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/abhi8923shriv/liver-disease-patient-dataset/code

The HTML formatted report can be found here on GitHub.