{"id":382,"date":"2021-07-03T00:00:00","date_gmt":"2021-07-03T00:00:00","guid":{"rendered":"https:\/\/tac.debuzzify.com\/?p=382"},"modified":"2023-06-27T06:30:11","modified_gmt":"2023-06-27T06:30:11","slug":"running-election-campaigns-with-k-means-clustering","status":"publish","type":"post","link":"https:\/\/www.the-analytics.club\/running-election-campaigns-with-k-means-clustering\/","title":{"rendered":"Running Election Campaigns With K-Means Clustering."},"content":{"rendered":"\n\n\n

Imagine that you are the chief campaign planner for the next presidential election. Thanks to the pandemic-driven technology adoption, campaigns are going online this time. Because it helps cover the entire nation without any traveling, your party decides to take innovative approaches.<\/p>\n\n\n\n

Your task is to find groups of people with similar interests (or needs). There is going to be a separate online campaign for each of them. How would you go about it if the future of your country depended on you?<\/p>\n\n\n\n

Data scientists use clustering algorithms to help with this problem. K-means is the simplest yet most effective algorithm to group large datasets using various properties. It\u2019s an iterative approach to finding non-overlapping groups in the dataset. In your case, finding distinct voters groups who share similar needs and interests. Each individual becomes a member of one and only one group.<\/p>\n\n\n\n

K is the number of clusters you prefer to have. But how do you know what the correct number is? Read through to find out.<\/p>\n\n\n\n

This article will take you through the steps to solve the puzzle and answer some of the crucial decisions you must make on your way. In this article, I\u2019ve used Python to implement the examples we discuss.<\/p>\n\n\n\n

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You can refer to the<\/i> GitHub repository<\/i><\/a> if you feel lost. The dataset I used for this illustration isn\u2019t original. If you still want it to practice, that\u2019s in the repository too.<\/i><\/p>\n<\/blockquote>\n\n\n\n

Finding clusters with two variables.<\/h2>\n\n\n\n

Let\u2019s suppose you have access to the age annual income (and debt) levels of individuals of your country (in a spreadsheet, like in the image below).<\/p>\n\n\n\n

\u00a0<\/p>\n\n\n

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\"Finding<\/figure><\/div>\n\n\n

Of course, you could create groups based on your prior knowledge. For example, millennials with high incomes would be one of the groups. But you decide to dive deep to see if there are other ways to cluster them. Hence you decide to perform K-means with the two available variables.<\/p>\n\n\n\n

Doing it using Python is effortless. You only need a few lines of code to read the data and perform K-means clustering.<\/p>\n\n\n\n