Dating R

What is R?

The 18th letter of alphabet??? Of course it is!! But I am writing about the other R. One of the MVP of Analytics.

R is a programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.

(source: Wikipedia) 

 First Hands on Experience with R

Few days back  another noob (me) started to learn R. Thanks to "Google" and "TryR" for providing basic ideas about the commands, functions, syntax and all. 

So, I earned all the badges for R on http://tryr.codeschool.com.

Later, one of my professors suggested to go through https://statsguys.wordpress.com/2014/01/03/first-post/ for the taste of a typical data analytics problem used for prediction for a knowledge program available on KAGGLE.

The problem is to develop a binary classification model to predict which passengers survived the sinking Titanic. In the problem, at first we worked to build an algorithm based on the parameters set in the training dataset then we applied the same algorithm on the test data set to get predict the survivors. 

Lessons Learned

The code is already provided in the above mentioned link, so we didn't have to pull our hairs out or scratch our head. As already mentioned, I am new to this field and I didn't understand many functions used in the code but I was able to understand their approach to the problem. Thanks to STATGUYS again for explaining each steps in detail.

The first step to approach any problem is data exploration. It gives you an idea about the data you are dealing with.

The next and the most critical part as per my opinion is cleaning the data. Implementing this process, we can remove the unwanted variables from my dataset and keep it simple. We can also take care of any missing values which generally create huge problems in results or we encounter horrendous errors. We can make inferences on the missing values based on the other variables related to that missing value.

The third part is training the model we have created. We have a test data set. Using that one, we can train the model to provide optimized and desired output. I am still in dark about the training process (may be using neural network or any other process). Surely, I will dig deep into it and and find out the secret recipe. After the training is done, we can implement the model for the test data set and get the output.

Here as you can see, I made my first submission on KAGGLE. I had to submit the excel sheet that was generated as the output for the test data set.

Anyone who is new to R or want to start R, I would suggest all of you to visit all the above links. It will help you a lot.  Others, wish me luck, so that I can do better and submit my own work on KAGGLE soon.  

Thank You.