thomasabigail / acm-research-coding-challenge

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ACM Research Coding Challenge (Fall 2020)

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.

Question Solution Explanation

Prior to this project, I had little experience with Python, but by researching K-Means and other clustering algorithms, I determined that the elbow method would be a proper way to find the number of clusters with the provided data. I went through many different iterations of code that had varying degrees of success, but I was able to create a successful program using the tutorial found here: https://youtu.be/imtvI5CQLm4 I programmed the project in Jupyter Notebook. At the beginning of the program, I imported the pandas module that reads in data from the file and helps the program print out data tables. Then, I imported the matplotlib in order to be able to print out the scatterplot with the .csv file's data. Lastly, I imported the MinMaxScaler and KMeans functions of the sklearn module in order to use the file's data to calculate the number of clusters. I created an object using the pandas module to read the cluster plot data, and then created a DataFrame of it. Next, I created an array followed by a for loop that created the graph determining the number of clusters. Lastly, I put the centroids into a data table and displayed them. Overall, this project was a great learning experience for me, and I'm happy to have stepped up to the challenge! Thank you for this opportunity.