praneeth300/Forest-Cover-Classification

A short description I want to give of how I am going to solve this project before starting. Our goal in this project is to classify which forest type it is from the data given. This study area includes 4 Wilderness Areas located in the Roosevelt National Forest of Northern Colorado. These area represent forests with minimal human-caused disturbances, so that existing forest cover types are more a result of ecological process rather than forest management practices. Each observation is 30m x 30m forest cover type determined from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from the data originally obtained from US Geological Survey (USGS) and USFS data. I have been given a total of 54 attributes/features, (excluding 1 target variable) these attributes contain Binary and Quantative attributes, and I need to predict which Forest Cover-Type is it from the given features. I will first explore the data, visualize it, know what the data wants to tell us. Remove any missing values and features that have null values and scale the data within a specific range. Also perform dimensionality reduction procedure where I will use 4 models to tell us which are useful in order to predict the target variable, and then using features which gives us hgih score in the most models. Those 4 Models are: Extra Trees Classifier (ETC) Random Forest (RF) AdaBoost Classifier (ADBC) Gradient Boosting Classifier (GBC) Split the data 75%-25%, train-test set respectively. Will use 10 K-fold Cross Validation on train set. Feed the training data to the Naive Bayes (Our Benchmark Model) and evaluate the result. Training will be done on the Solution Models that I have chose, those are:- K-Nearest Neighbour (KNN), Random Forest (RF), Stochastic Gradient Descent Classifier (SGDC), Extra Trees Classifier (ETC), Logistic Regression (LG)

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