Machine Learning Project : Forest CoverType Prediction

Machine Learning Project : Forest CoverType Prediction

Predicting the forest cover type using various types of models i.e. linear Classifier(Naive Bayes Classifier), tree based Classifier(Random Forest Classifier), clustering(K-Means Classifier), etc. Also, we will combine feature selection methods such as Lasso, Random Forest, Principal Component Analysis with each of the classifier models. The idea is to compare the accuracy of each model and how the feature selection method affects each of the classifier. Using this analysis, we will ultimately try to improve the accuracy for forest cover type prediction and give the best suitable combination of feature selection method and a classifier with the highest accuracy.