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 Liver Disease Patients 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: After 50 trials, the best AutoKeras model processed the training dataset with the best accuracy score of 99.78%.
CONCLUSION: In this iteration, AutoKeras 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.