Ayush Adhikari

My personal website

Ayush Adhikari

 > 

Search
Monday, May 19, 2025

Found 3 result(s) for "cluster"! Click on the links for more details

Distributed_Analytics_of_US_Residential_Zoning

This is a project that aims to do distributed analytics using clusters using a spatial dataset. Our goal with this project was to analyze the impact of single family rresidential zoning in the US and correlate it to quality of life measures in an effort to dissuade a segreggation of zoning types and promote inclusivity. We hoped to be able to compare the results against data from other countries that have more includive zoning laws, but this was not possible due to constraints on data availability and language barriers. For the distributed component, we are using a cluster of 10 machines that are managed by Yarn. To do the processing of data and calculations, we applied Spark using Java and Gradle. The data itself was stored using HDFS and totaled to ~3.2 GB. For more detail on our motivation, procedures, project structure, and results, please reference the latex file or the presentation in the GitHub repo.

Analysis of the MovieLens Dataset using Apache Spark

This project was an introduction to using Apache Spark to analyze a large file (~800 MB), namely the Movie Lens dataset containing movies, genres, ratings, etc. The files were stored using HDFS and cluster size consisted of 10 machines. There is 1 Java file with 7 Spark jobs which are focused on answering the 7 questions that can be found on GitHub.

Analysis of Million Songs Dataset using Hadoop MapReduce

This project was an introduction to using Hadoop MapReduce to analyze a large file (~1.6 GB), namely the Million Song subset containing 10,000 songs. The files were stored using HDFS and cluster size consisted of 10 machines. There are 10 Java files with jobs of their own which are focused on answering the 10 questions below. Please visit the github for more details on the questions, answers, and more.

Nothing to show!
×

This is a feature mainly used to demonstrate the use of the handwriting app built using a canvas and CNN. You can use the search bar to search the site for content or to navigate the site by typing in a word such as home, which will take you to the home page. Below the search bar is a canvas where you can draw letters. This is implemented using a digit recognition CNN. As the accuracy is around 89 percent, it might not always produce an accuracte prediction, for example between o and 0. You can use the three button next to the search bar to delete the last character, clear the search bar, and finally search the site.