Machine Learning Model Evaluation

So, the Solution is we need to split our data into training and testing sets. Training data (in-sample data) will be used to train our model and test data (out-of-sample) will be used for testing our model performance, this data will evaluate how our model performs on new sets or in real world. When we split a data, usually 70 percent of the data for training and 30 percent for testing. We will use sklearn for splitting our data, consider below code. And to help and validate our model python has a function which can be imported from sklearn library.