This Machine Learning Challenge requires participants to create predictive models to predict the stage of liver Cirrhosis using 18 clinical features. Cirrhosis damages the liver from a variety of causes leading to scarring and liver failure. Hepatitis and chronic alcohol abuse are frequent causes of the disease. Liver damage caused by cirrhosis can't be undone, but further damage can be limited. Treatments focus on the underlying cause. In advanced cases, a liver transplant may be required. Predicting the stage of cirrhosis and beginning the treatment before it's too late can prevent the fatal consequences of the disease.
In this project we used 3 different types of data analytics to solve the problem.
Descriptive Analysis - This helps to understand what happened in the past in the data.
Diagonistics Analysis - This helps to identify the common trend in the past in the data.
Predictive Analysis - predicting for the future occurence to help identify the Liver stage.