Analytics Vidhya Workshop / Hackathon – Experiments with Data

This was a hackathon + workshop conducted by Analytics Vidhya in which I took part and made it to the #1 on the leaderboard. The data set was straight-forward and quite clean with only a minor need for missing value treatment. This post will might be useful for people who want a walk-through on the steps involving data munging and developing machine-learned models.

screenshot-datahack.analyticsvidhya.com 2016-09-01 23-43-54

 

The workshop ended with a basic hackathon with data given on age, education, working class, occupation, marital status and gender of individuals and one had to predict the income bracket of these individuals.

I’ve posted the data and my code and solutions in this GitHub repo. An IPython Notebook has also been shared.

I approached the problem first by attempting some feature engineering (other than missing value treatment) on the data, and then ran a basic logistic classifier and a random forest classifier. However it turned out that these models performed better without feature engineering, which shows the dataset was already quite clean and informative to begin with for this competition.

I later attempted gradient boosting with parameter tuning to maximizing scores.

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Solutions to Machine Learning Programming Assignments

This post contains links to a bunch of code that I have written to complete Andrew Ng’s famous machine learning course which includes several interesting machine learning problems that needed to be solved using the Octave / Matlab programming language. I’m not sure I’d ever be programming in Octave after this course, but learning Octave just so that I could complete this course seemed worth the time and effort. I would usually work on the programming assignments on Sundays and spend several hours coding in Octave, telling myself that I would later replicate the exercises in Python.

If you’ve taken this course and found some of the assignments hard to complete, I think it might not hurt to go check online on how a particular function was implemented. If you end up copying the entire code, it’s probably your loss in the long run. But then John Maynard Keynes once said, ‘In the long run we are all dead‘. Yeah, and we wonder why people call Economics the dismal science!

Most people disregard Coursera’s feeble attempt at reigning in plagiarism by creating an Honor Code, precisely because this so-called code-of-conduct can be easily circumvented. I don’t mind posting solutions to a course’s programming assignments because GitHub is full to the brim with such content. Plus, it’s always good to read others’ code even if you implemented a function correctly. It helps understand the different ways of tackling a given programming problem.

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Enjoy!