Model Free Prediction and Regression : A Transformation Based Approach to Inference The Model Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with e.g. i.i.d. or Gaussian. As such it restores the emphasis on observable quantities i.e. current and future data as opposed to unobservable model parameters and estimates thereof and yields optimal predictors in diverse settings such as regression and time series. Furthermore the Model Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model based paradigm i.e. (a) fit a model to the data at hand and (b) use the fitted model to extrapolate predict future data. Due to both mathematical and computational constraints 20th century statistical practice focused mostly on parametric models. Fortunately with the advent of widely accessible powerful computing in the late 1970s computer intensive methods such as the bootstrap and cross validation freed practitioners from the limitations of parametric models and paved the way towards the `big data era of the 21st century. Nonetheless there is a further step one may take i.e. going beyond even nonparametric models; this is where the Model Free Prediction Principle is useful. Interestingly being able to predict a response variable Y associated with a regressor variable X taking on any possible value seems to inadvertently also achieve the main goal of modeling i.e. trying to describe how Y depends on X. Hence as prediction can be treated as a by product of model fitting key estimation problems can be addressed as a by product of being able to perform prediction. In other words a practitioner can use Model Free Prediction ideas in order to additionally obtain point estimates and confidence intervals for relevant parameters leading to an alternative transformation based approach to statistical inference Prediction: some heuristic notions The Model free Prediction Principle Model based prediction in regression Model free prediction in regression Model free vs. model based confidence intervals Linear time series and optimal linear prediction Model based prediction in autoregression Model free inference for Markov processes Predictive inference for locally stationary time series Model free vs. model based volatility prediction