This is a complete example for the Course Project in "Developing Data Products" on Coursera. The Shiny application is a simple Body Mass Index (BMI) calculator, which takes user input for height and weight and outputs the BMI value along with a health classification. This meets the requirements for creating an interactive data product.

This presentation showcases interactive data visualizations created using Plotly in R Markdown. We'll explore scatter plots, 3D visualizations, bar charts, and box plots with sample data.

Overview This document summarizes the work done for the Prediction Assignment Writeup project for the Coursera Practical Machine Learning course. It's created using the functionalities of the knitr package in RStudio using the actual analysis code. The repository for this work can be found at https://github.com/amete/PracticalMachineLearningAssignment. Background Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it. In this project, your goal will be to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways. More information is available from the website here: http://groupware.les.inf.puc-rio.br/har (see the section on the Weight Lifting Exercise Dataset).

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.

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.