With the help of a technology called Machine Learning, we can create a system where we can gain access to the Social Media post and identify which post are about an accident. Through a Social Media called Twitter, we can access their tweeted text by scrapping the text using twitter API service. The scrapped tweeted text will then be processed in a way for us to gain the keywords from the text where it is related to the accident. The keywords will then be used to create a Machine Learning model to identify the tweeted text which contains information about an accident and give early warning to the authorized party to act as fast as possible.

The way Spotify application works where it recommends a music based on the songs in our playlist might be done or can be done by using Unsupervised Machine Learning. The Unsupervised Machine Learning method such as K-means can cluster numerical data without any target. As my goal for this project, where I use a Spotify song data lists with many variables that describe the songs, to be able to recommend songs which are similar to the songs that we are frequently listen, K-means method is suitable for this case.

This RmD is about Machine Learning prediction using Naive Bayes, Decision Tree, and Random Forest to predict type of drug best given to a person with a certain type of condition from a data result from an experiment. This RmD is also made as a way for me to learn how to predict using the three previously mentioned Machine Learning modeling method and as a fulfillment for my assignment for Algoritma Data Science School.

As a way to review my lecture and to complete an assignment from Algoritma Data Science School, I made my second RmD regarding Logistic Regression and K-NN modeling using a data where it contains only numerical variables

As a way to learn by building and as a fulfillment for my assignment in Algoritma Data Science School, I published my RmD where I model a credit card appoval data using Logistic Regression and K Nearest Neighbor method for my machine learning modeling

As a way to fulfill my assignment for Algoritma Data Science School, I would like to create a model to predict the Manufacturer's Suggested Retail Price of car models from different kind of company brands by using many specification of each model as the predictor for the model that I am about to make. I will then make a prediction based on the model, do a validation test to the model whether the model is acceptable or need some adjustment, and make an interpretation of the model.

This is my second data analysis attempt to learn about analyzing data using R language and as a fulfilment for my assignment in my data visualization in Algoritma Data Science School

This document is made as way for me to practice on learning how to create a report through R markdown. The dataset used in this report was obtained from Kaggle.com. See the reference section inside the report to get the link

A published document result of my R programming language class in Algoritma Data Science School