How to build machine learning models with Python

Now that you have explored and understood your data, it is time to select the appropriate machine learning model. The model to be chosen would depend upon the type of problem you are trying to solve.These are some of the common problem along with their corresponding model categories:Classify: If you need to predict a category or class label, such as spam versus non- spam emails, then you would want to use a classification model, and Scikit-learn has options from basic ones like support vector machines and random forests through those a bit more complex like K-nearest neighbors.Regression: If you are solving a problem where you need to explain a continuous value for example, house or stock price then it’s best to use a regression model. First, this would be linear regression or either a decision tree or polynomial regression will work.Feel free to run other models on your data. Scikit-learn has a consistent interface for running many algorithms.Now that you’ve selected a model, we have to break our data down into two fundamental sets: Training and test data. If you will, think of your data as a deck of a thousand flashcards. Training Data is that deck of flashcards that will be used to build learning models with python. Your model will learn this dataset for patterns and relationships. Testing Data is much like another deck you had set aside just for quizzing. Once trained, this model gets tested to show how it generalizes to unseen data.Model training is the process of passing the training data through the model, which learns fundamentally the underlying patterns or relationships in the data. It may, therefore, predict new, unseen data points.These metrics will inform you how good or how bad your model is performing. 

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