{"version":"1.0","type":"rich","title":"Gist 6b851197754b1a378f905e75b82697b0","description":"This example demonstrates how to engineer and transform date, time, categorical, and numerical features for predictive modeling. The R script defines date-based variables such as day of week, week of year, and quarter, alongside time bins for 24-hour periods, and incorporates open calendar data like national holidays and seasons. It applies categorical encodings via hashing with FeatureHashing's `hashed.model.matrix` using a 14-bit hash, one-hot, target, orthogonal polynomial, and deviation methods. Numerical transformations include Box-Cox and binning, while general manipulations support column-wise or combinatorial diff, sum, prod, and custom formula operations across data frames.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-6b851197754b1a378f905e75b82697b0?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}