Rahul Agarwal
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  • Bangalore
  • India
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Short Bio:
Senior Statistical Analyst at Walmart Labs
Job Title:
Senior Statistical Analyst
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Networking, Other

Rahul Agarwal's Blog

What my first Silver Medal taught me about Text Classification and Kaggle in general?

Posted on April 19, 2019 at 10:15am 0 Comments

Kaggle is an excellent place for learning. And I learned a lot of things from the recently concluded competition on Quora Insincere questions classification in which I got a rank of 182/4037. In this post, I will try to provide a summary of the things I tried. I will also try to summarize the ideas which I missed but were a part of other winning solutions.

As a side note: if you want to know more about NLP, I would…


Using XGBoost for time series prediction tasks

Posted on December 18, 2018 at 9:30am 0 Comments

Recently Kaggle master Kazanova along with some of his friends released a "How to win a data science competition" Coursera course. The Course involved a final project which itself was a time series prediction problem. Here I will describe how I got a top 10 position as of writing this…


Top Data Science Resources on the Internet Right Now

Posted on December 27, 2017 at 5:00am 7 Comments

I have been looking to create this list for a while now. There are many people on quora who ask me how I started in the data science field. And so I wanted to create this reference.

To be frank, when I first started learning it all looked very utopian and out of the world. The Andrew Ng course felt like black magic. And it still doesn't cease to amaze me. After all, we are predicting the future. Take the case of Nate Silver - What else can you call his success if not Black…


How to think like a Data Scientist

Posted on March 29, 2017 at 10:00am 3 Comments

A data scientist needs to be Critical and always on a lookout for something that misses others. So here is some advice that one can include in the day to day data science work to be better at their work:

1. Beware of the Clean Data Syndrome

You need to ask yourself questions even before you start working on the data. **Does this data make sense?** Falsely assuming that the data is clean could lead you towards wrong Hypotheses. Apart from that, you can discern a…


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