Forest Cover Type Prediction: A Comparative Study of ML Algo
Implements and compare kNN, SVM, and Random Forest algorithm
Implemented and compared kNN, SVM, and Random Forest algorithms for predicting forest cover types using Python and scikit-learn
Preprocessed and normalized large-scale geographical data from satellite imagery
Achieved highest accuracy of 82% using Random Forest, outperforming kNN (79.7%) and SVM (72.6%)
Developed skills in data preprocessing, model selection, performance evaluation, and data visualization
Utilized libraries such as NumPy, Pandas, and Matplotlib for data manipulation and result visualization
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