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.

« Visualisation des coûts DQE et volumes des ouvrages d’une STEP 1000 lits. »

un courbe

Wearable devices such as Fitbit, Nike FuelBand, and Jawbone Up have made it easy to track how much physical activity people perform, but rarely how well they perform it. In this project, we use accelerometer data collected from the belt, forearm, arm, and dumbbell of 6 participants performing unilateral dumbbell biceps curls in 5 different ways: Class A: Correct execution (according to specification) Classes B–E: Common mistakes (throwing elbows, lifting dumbbell only halfway, lowering only halfway, throwing hips) The goal is to predict the "classe" variable using any of the other predictors, build a robust model with cross-validation, estimate out-of-sample error, and finally predict the outcome for 20 test cases.