I recently participated in this competition (WIDS Datathon by Stanford) where I was able to land up in Top 10 using various boosting algorithms. Since then, I have been very curious about the fine workings of each model including parameter tuning, pros and cons and hence decided to write this blog 

Well, in this post, I will be discussing the usefulness of each error metric depending on the objective and the problem we are trying to solve. When someone tells you that “USA is the best country”, the first question that you should ask is on what basis is this statement being made. Are we judging each country on the basis of their economic status, or their health facilities etc.? Similarly each machine learning model is trying to solve a problem with a different objective using a different dataset and hence, it is important to understand the context before choosing a metric

In the first blog, we discussed some important metrics used in regression, their pros and cons, and use cases. This part will focus on commonly used metrics in classification, why should we prefer some over others with context.

One of the most common problems I have faced in Data Cleaning/Exploratory Analysis is handling the missing values. Firstly, understand that there is NO good way to deal with missing data. I have come across different solutions for data imputation depending on the kind of problem — Time series Analysis, ML, Regression etc. and it is difficult to provide a general solution. In this blog, I am attempting to summarize the most commonly used methods and trying to find a structural solution.