ACM Research Coding Challenge (Fall 2020)

• My answer and explanation are located at the bottom of the README *

No Collaboration Policy

You may not collaborate with anyone on this challenge. You are allowed to use Internet documentation. If you do use existing code (either from Github, Stack Overflow, or other sources), please cite your sources in the README.

Submission Procedure

Please follow the below instructions on how to submit your answers.

1. Create a public fork of this repo and name it ACM-Research-Coding-Challenge. To fork this repo, click the button on the top right and click the "Fork" button.
2. Clone the fork of the repo to your computer using . git clone [the URL of your clone]. You may need to install Git for this (Google it).
3. Complete the Challenge based on the instructions below.
4. Email the link of your repo to [email protected] with the same email you used to submit your application. Be sure to include your name in the email.

Question One

Given the following dataset in ClusterPlot.csv, determine the number of clusters by using any clustering algorithm. You're allowed to use any Python library you want to implement this, just document which ones you used in this README file. Try to complete this as soon as possible.

Regardless if you can or cannot answer the question, provide a short explanation of how you got your solution or how you think it can be solved in your README.md file.

Answer/Explanation:

There are 2 clusters

I know a little from learning over the summer, but I did some additional research to enhance my understanding of clustering algorithms. I used a Gaussian Mixture Model clustering algorithm. I chose this algorithm over the K-means model because of 2 advantages: 1) a GMM allows oval-shaped/stretched clusters instead of just circular clusters and 2) provides the probabilities that the data points point to a certain cluster. I used the sklearn (sci-kit learn) library, specifically sklearn.mixture.GaussianMixture. The GaussianMixture allows for a model to be created given the number of components and covariance type (I used 'full'). To determine the optimal number of clusters/components, I learned that this the value that has the lowest Akaike information criterion (AIC) or Bayesian information criterion (BIC). I was able to find these values for up to 15 clusters, determining that both 2 and 3 clusters can be reasonably found in the dataset. However, to choose one out of the two, I looked at the probabilities that each data point belongs in a cluster using gmm.predict_proba(dataset) and noticed that data points in 2 clusters were much more likely than in 3. Therefore, I concluded that there are 2 clusters in the dataset.

Libraries I used: numpy, pandas, sklearn.mixture, matplotlib.pyplot

I adapted my code from the following article: https://towardsdatascience.com/gaussian-mixture-models-d13a5e915c8e

MLA Citation of source: Maklin, Cory. “Gaussian Mixture Models Clustering Algorithm Explained.” Medium, Towards Data Science, 16 July 2019, towardsdatascience.com/gaussian-mixture-models-d13a5e915c8e.