moizali-dev / moizali-dev.github.io

Portfolio by Moiz Ali

This portfolio is a compilation of projects, notebooks and MOOCs completed

Stand-alone projects

Spotify Music Genre Classification

This is my own end to end project using the Spotify API to predict the genre of a song/track This project consists of 2 parts: The Data Engineering and the Machine Learning/Deployment part

Part1: The Data Engineering part related to the architecture, challenges, solutions and next steps are here.
Part2: The Machine Learning/Deplyment part related to the model used, deployment architecture and next steps are here

Live version is here

Demo:

Demo.mov

Credits:
DataTalksClub, TheSoundofAI, KodeKloud

Volunteer projects (DataKind)

Risk of student dropping out

Worked on a project to predict whether a student will be at risk of dropping out or not before joining JJC.
Case study of the project can be found here

Cost of brownfield in US

Worked in a team on a project to predict the cost of a specific brownfield in U.S. based on environmental, climate and demographics dataset. Project involved cleaning the datasets, presenting findings to the stakeholders, building a pipeline (using tidymodels in R) and having it ready for production following best practices of code documentation.
Case study of the project can be found here

Link: DataKind

Kaggle notebooks

PASSNYC Clustering Analysis (3rd place Winner for top Notebook)

One of the first Kaggle competitions in which there was opportunity to help out a not for profit company. Managed to conduct extensive EDA and gather insights using clustering methodologies. Here is the notebook

Home Credit Default Risk

Used the opportunity to go through the Applied Predictive Modelling book and apply it to the competition. The notebook got a gold rating and can be found here

MOOCs and Relevant Courses

• Introduction to Programming Using Python (Edx)
• Introduction to Object Oriented Programming Using Java (Edx)
• Data Structures and Algorithms (Edx)
• Machine Learning Foundations: A Case Study Approach (Coursera)
• Neural Networks and Deep Learning (Coursera)
• Computer Science I (Toronto Metropolitan University)
• Computer Science II (Toronto Metropolitan University)
• Unix/C/C++ (Toronto Metropolitan University)