Kaggle-Classfication-Competition

Conducted predictive classification modelling and performance evaluation for several models used to predict the political affiliation (target variable) of random U.S citizens.

1. Administered EDA using Tidyverse and ggplot2 in R Studio to discover the best predictors of political affiliation with regards to correlation on the training dataset

2. Built, trained, tuned and tested classification models including logistic, KNN, random forests, LDA, QDA and SVM, to best predict the political affiliation of a random U.S citizen in terms of accuracy

3. Achieved an accuracy of 65.08 % using a tuned LDA model on the validation dataset (top 20% in the world)