This is the final part of the Gerrymandering series in which I will discuss a clustering technique that is used to generate a district plan.

In the previous blogs, we discussed what gerrymandering is, how it is harmful for democracy and saw how gerrymandered the 2012 plan for PA was. Now, we will see if introducing machine learning into this aspect improves the plan or not. For now, we will be focussing on clustering technique.

The main goal with clustering was to create a plan in which I can get considerably compact and fair (based on voter distribution) districts while maintaining an even distribution of population across all districts. For that, I chose k-means clustering. Initially, I implemented a classic k-means clustering (i.e. …


This is part 2 of Gerrymandering series in which , we will do more analysis and focus on PA state.

In the previous part, we discussed what gerrymandering is and the ways we can detect it. Now, in this part, we will discuss those ways in detail by investigating the 2012 plan for PA and comparing it with the 2018 (remedial) plan.

First of all, let’s look at the population distribution.

this image shows how population is unevenly distributed across the districts in the 2012 plan.
this image shows how population is unevenly distributed across the districts in the 2012 plan.


This is part 1 of Gerrymandering series in which we will discuss what gerrymandering is,how it impacts elections and how it can be detected.

Every ten years, the United States conducts a census, and from this population information, states create congressional districts, and from each of these districts, a representative is elected to represent the people of that district. However, these districts are not always drawn to be fair and truly representative. And that is caused by gerrymandering. But what is it actually? By definition,

It is a practice in which the politicians at the power will create advantage for their party by intentionally creating boundaries in such a way that they get the lead by distributing voters in their favor.

The term was…


Recently, I had an assignment of explaining my major to an 11-year old kid (based on FLAME Challenge). This is my take on explaining Data Science.

Imagine you are a detective and is assigned to solve a crime. What would be your approach? From the shows you have seen and the stories you have read, you will know first of all, you will look for evidences. Then you will work on it and see if there is meaning coming out of all this. After that, you will see to which person does it all point to. Finally, you will present all your theory to authorities and if turned out to be correct, you win the case.

Just like that, there is another field where you apply…

RAHUL NAIR

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