Stack: Python Pandas 
Scikit-learn
I developed a decision tree machine learning model to predict loan approvals. By analyzing key applicant information such as income, credit score, loan amount, and employment status, the model accurately predicts if a loan application would be approved or not. This predictive tool empowers lenders to make informed decisions efficiently and optimize the loan application process.
Stack: Python Pandas Scikit-learn ANN
I built an Artificial Neural Network (ANN) model to predict the success of rookies in the NBA after 5 years. By analyzing various performance metrics from other succesful players in their rookie season such as points per game, rebounds and more, the model predicts the likelihood of a rookie becoming successful in the NBA. This tool provides valuable insights for NBA teams to make informed decisions when developing rookies.
Stack: Python Pandas Scikit Linear Regression
I built a multiple linear regression model to predict house prices. By analyzing various features such as square footage, number of bedrooms, bathrooms, location, and other relevant factors, the model accurately predicts the price of a house. This model can be valuable for home buyers, sellers, and real estate professionals.
Stack: Python Pandas Scikit-learn NLTK
This project implements movie sentiment analysis by scraping reviews and ratings from the IMDB website. It utilizes the VADER sentiment analyzer to detect if a movie review is positive, negative or neutral. Then uses decision tree and SVM classifier ML algorithms to predict the sentiment of new movie reviews.