Based on data from *World Health Organization (WHO)*, number of people with diabetes has risen from 108 million in 1980 to 422 million in 2014 and the global prevalence of diabetes among adults over 18 years of age has risen from 4.7% in 1980 to 8.5% in 2014. Obviously we know that diabetes is a major cause of blindness, kidney failure, heart attacks, stroke and limb amputation. However, diabetes can be treated and its consequences avoided or delayed with diet, physical activity, medication and regular screening and treatment for complications. Furthermore, this analysis aims to predict Diabetes from diagnostic measurement. The following dataset is originally donated to the UCI Machine Learning Repistory and organised by Friedrich Leisch. It contains 768 observations and 9 variables.

"Without music, life would be a mistake", Friedrich Nietzsche It is undoubted that music becomes one important thing of people nowadays as they can access music in everywhere and anytime. As one of music online platform, Spotify is one of popular music platform that most used by people around the world. Every year, spotify launch the 50 most popular songs by singer, genre and etc. Furthermore, in this analysis I would like to group those 50 top songs of 2019 into several category in order to see the type of the song. The data was taken from https://www.kaggle.com/leonardopena/top50spotify2019.

Happiness level in each country is determined by several factors. In this analysis I analyzed the determinant of *happiness* using a dataset from `kaggle.com`. In which, the dataset consist of several macroeconomics variables such as GDP, healthy life expectancy, happiness score, freedom of choice and generosity. The happiness score will be analyzed using linear regression model.

This research is about forecasting total likes given by an account in Instagram. This compares time-series Holt-winters and auto ARIMA forecasting to see the better result of forecasting.

This analysis is to compare between IMDB rating and total income of Marvel and DC. Marvel wins on Income, however based on quality (IMDB rating), DC beats Marvel!!

Grouping TV shows and Movies on Netflix based on country released and rating.

The data is about biography of passengers of Titanic. It contains information about gender, who survived or not, ticket class, age, total sibling that aboard the titanic, number of parents aboard, passenger fare, and port of embarkation. This research is to predict the rate survival of Titanic’s passenger based on those thing. This research will use two models, logistic regression and KNN analysis. At the end of this analysis, this will compare performance between logistic and KNN model. The steps of the analysis are as followed :

This is working project. Analysis who did survive on Titanic based on gender, port embarked and passenger's class.

This is to analyse correlation between Jobs and Gender including how it impacts to their salaries.