Forest-Cover-Type-Classification

In this paper, we analyze the Forest Cover Dataset provided by UCI, build numerous classifiers to classify the forest cover type of a parcel of land, and evaluate the performance of the classifiers. To achieve this goal, we applied a step by step approach to the problem and explained the process thoroughly. We start the process with the data preprocessing. After adding id column and headers to the data in MS Excel, the data is stored in a “Comma Seperated Value” (.csv file) format. Following, definition of the libraries used in this project are provided. After applying data processing to the dataFrame, we examined and explained some features about the data that is crucial in the classification processes. In this process; plot of the data, correlation in the data columns, distribution of the data are the main focus. Furthermore, data is classified by different classifiers and these classifiers are cross validated with several hyperparameters to find the best tuning for each classifier. Afterwards we trained the classifiers with best hyperparameters and tested the performance on data. Finally we compared and analyzed the results.