Machine Learning Project The goal of this project is to evaluate the efficacy of different models and algorithms in attempting a binary classification problem while avoiding overfitting, with a particularly difficult data set for which overfitting is quite natural. To do this, multiple approaches are considered and implemented. The motivation for this project is to objectively evaluate the ability of different models and algorithms to handle problems in which overfitting is a severe concern, given the dataset.This project is inspired by a Kaggle challenge, Don’t Overfit II. All the results obtained are by submitting our predictions on Kaggle.
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