Senior Software Engineer |  MSc City, University of London

github

Mohd Arif  • Peerlist

<p>Most great jobs never make it to job portals. FeedHire surfaces hidden job opportunities from social platforms and communities into one clean dashboard. With a brand-new UI, personalized dashboard, and flexible pricing, FeedHire helps you focus on signal, not noise.</p>

FeedHire

In this project, I explore how machine learning models can be used to predict the popularity of music songs. For my study, I've utilized Spotify data, which includes two sets: one containing music features, and the other external factors concerning artists. Based on this general approach, I sought to identify the subtle interactions between different track shares and relative listener interest. I use these two data sets to analyze patterns and relationships with the highest effect on track success from my machine learning model, in addition to gaining new insights into the inner workings of this popular music.

Spotify Song's Success Prediction using Machine Learning

This project conducts a detailed analysis of road traffic accident hotspots across the United Kingdom, harnessing multivariate data to uncover the patterns and regularity of such events. Geared towards aiding highway operators and Public Work Department officials, I utilize data visualization techniques, primarily through Tableau and Python, to transform complex data sets into intelligible visual stories. These visualizations assist in recognizing trends and pinpointing areas of high accident frequency, thereby informing and refining traffic management and road safety strategies. The insights I derive are anticipated to significantly influence the creation of preventative measures, streamline road safety practices, and ultimately enhance the public's welfare and the efficacy of road network management.