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in this video we are going to talk about how to choose the best model for your given machine learning problem and how to do hyper parameter tuning here is a list of topics that we are going to cover in this video lets say you are trying to classify SK learns iris flower data set where based on the petal and sample width and length you are trying to predict what type of flower it is now the first question that arises is which model should I use there are so many to choose from and lets say you figured out that SVM is the model I want to use the problem doesnt end there now you have hyper parameters what kind of kernel and C and gamma of LSU should I be using there are just so many values to choose form the process of choosing the optimal parameter is called hyper tuning in my jupiter notebook i have loaded iris flower data set here and it is being shown in a table format the traditional approach that we can take to solve this problem is we use trained test split method to split our
