* There are four assumptions associated with a linear regression model: Linearity: The relationship between X and the mean of Y is linear. Homoscedasticity: The variance of residual is the same for any value of X. Independence: Observations are independent of each other. *. The term ‘Boosting’ refers to a family of algorithms which converts a weak learner to a strong learner. * Bagging is a way to decrease the variance in the prediction by generating additional data for training from dataset using combinations with repetitions to produce multi-sets of the original data. *Regularisation means that our model works well not only with training or test data, but also with the data it'll receive in the future. In summary, to achieve this, regularization It means that our model works well not only with training or test data, but also with the data it'll receive in the future. In summary, to achieve this, regularization shrinks the weights toward zero to discourage complex models. * In the Stepwise regression technique, we start fitting the model with each individual predictor and see which one has the lowest p-value. Then pick that variable and then fit the model using two variable one which we already selected in the previous step and taking one by one all remaining ones. *Feature selection is Feature selection is the process of identifying and selecting a subset of input variables that are most relevant to the target variable. Perhaps the simplest case of feature selection is the case where there are numerical input variables and a numerical target for regression predictive modeling. *The great advantage of regression models The great advantage of regression models is that they can be used to capture important relationships between the forecast variable of interest and the predictor variables. A major challenge however, is that in order to generate ex-ante forecasts, the model requires future values of each predictor. A major challenge however, is that in order to generate ex-ante forecasts, the model requires future values of each predictor