PENGENALAN PROGRAM R

This project focuses on building a machine learning model that can accurately predict the manner in which individuals perform weight-lifting exercises. The dataset contains sensor measurements collected from accelerometers placed on different parts of the body. These measurements capture movement patterns, which are then used to classify how correctly or incorrectly each exercise was performed. The overall workflow includes downloading and cleaning the dataset, removing irrelevant or noisy variables, exploring correlations, splitting the data into training and validation sets, training a predictive model using Gradient Boosted Machines (GBM), evaluating performance through a confusion matrix, identifying important predictors, and generating final predictions for the test set. The goal is to create a reliable model that generalizes well to unseen data.

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