Analysis with regression models to predict rainfall in Australia.
Link to the R notebooks.
Two separate measures of job performance are presented in this case study. First, the supervisors for each of the 147 study participants were asked to rate how well their employee(s) perform on the physical aspects of their jobs using a 60-point scale. Higher numbers indicate better performance on the physically demanding aspects of the job. Second, two work simulations were developed by using information collected from an analysis of the jobs as well as observations and measurements of individuals performing each of the jobs studied. The simulations required that the participant exert force on a simulated wrench while assuming a standing and a kneeling position. The scores on each of the simulations were standardized and summed together to form one single measure. Larger scores indicate better performance on the work simulations.
Predict next-day rain by training classification models on the target variable from the dataset. The dataset contains a combination of categorical, nominal, and seasonal features. Standard methods such as logistic regression will be useful on this dataset.
Data source : https://www.kaggle.com/jsphyg/weather-dataset-rattle-package/download (Rainfall in Australia)