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Successfully developed a machine learning model which can accurately predict whether a firm will become bankrupt or not, depending on various features such as net value growth rate, borrowing dependency, cash/total assets, etc.

The Employee Attrition Control project uses data analysis and predictive modeling to understand and address employee turnover. It provides insights and recommendations to reduce attrition and improve employee satisfaction and retention.

CHL5230 - Applied Machine Learning for Health Data (Fall 2023) @ University of Toronto: Datathon #1 - Lifestyle and Health Factors

Data was downloaded through Kaggle

Successfully established a machine learning model which can accurately predict whether an employee of a given company will leave it in the impending future or not, based on several employee details and employment metrics.

Successfully developed a machine learning model which can accurately predict up to 100% accuracy whether a credit card application of a given applicant would be approved or not, based on several demographic features such as applicant age, total income, marital status, total years of work experience, etc.

Successfully created a machine learning model which can accurately predict the fare of a taxi trip based on several features such as trip duration, tip amount, etc.

CHL5230 - Applied Machine Learning for Health Data (Fall 2023) @ University of Toronto: Datathon #5 - mHealth and ML

Successfully established a text summarization model using Seq2Seq modeling with Luong Attention, which can give a short and concise summary of the global news headlines.

Data analysis, recommendation system & sentiment analysis for Airbnb listings in Athens, Greece

Explored a dataset of planes while learning PySpark commands.

The objective of this project is to predict the number of bicycles needed to be made available each hour in order to make the service as efficient as possible

CHL5230 - Applied Machine Learning for Health Data (Fall 2023) @ University of Toronto: Datathon #4 - ICU Mortality Prediction Using ML

CHL5230 - Applied Machine Learning for Health Data (Fall 2023) @ University of Toronto: Datathon #2 - Early Prediction of Heart Failure

Successfully established a machine learning model using PySpark which can accurately classify whether a bank customer will churn or not up to an accuracy of more than 86% on the test set.

An SQL-based exploration of COVID-19 data and vaccination progress using the Covid-Deaths dataset for insights into global pandemic trends.

A Machine Learning Project implemented from scratch which involves web scraping, data engineering, exploratory data analysis, NLP processing and ML, achieving the functionality of a Content based movie recommender system

Prediction of happy Customers based on Happiness Survey Data