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<speak> Welcome to the lesson on Model training and prediction. <break strength="x-strong"/> The next part is building the model. <break strength="x-strong"/> What is a machine learning model?<break strength="x-strong"/> A machine learning model is a file that has been trained <break strength="weak"/>to recognize certain types of patterns. <break strength="x-strong"/> You train a model over a set of data, <break strength="weak"/>providing it an algorithm that it can use to reason over and learn from those data.<break strength="x-strong"/> To relate input and output to predict the output variable, <break strength="weak"/>we need to build a machine learning model. <break strength="x-strong"/> The model is an equation relating to input and output having parameters. <break strength="x-strong"/> We use the algorithm to find the parameters of the model. <break strength="x-strong"/> In the first step, you need to assume that input <break strength="weak"/>and output are related by some equation that includes parameters.<break strength="strong"/> From the algorithm,<break strength="weak"/> we can find the parameters of the model. <break strength="x-strong"/> There are four steps in building the model. <break strength="x-strong"/> The first step is to preprocess the data.<break strength="x-strong"/> The second step is model selection and training.<break strength="x-strong"/> The third step is the prediction from the model.<break strength="x-strong"/> The fourth step is the model evaluation.<break strength="x-strong"/> What is preprocessing data?<break strength="x-strong"/> Data preprocessing is a data mining technique <break strength="weak"/>that is used to transform the raw data into a useful and efficient format.<break strength="x-strong"/> Let us see preprocessing the data.<break strength="strong"/> Sometimes the data we are presenting to the model is required to be pe-processed. <break strength="strong"/>That means you have to do normalization. <break strength="strong"/>Sometimes we have to separate them into training and testing data.<break strength="strong"/> Some columns have to be Transformed. <break strength="x-strong"/> You need to separate input and output data. <break strength="strong"/>In the data frame, <break strength="weak"/>we drop the chance of admit and store that data in x.<break strength="strong"/> To drop a column, we use data.drop.<break strength="x-strong"/> Here x is the input variable, <break strength="weak"/>and y is the output variable.<break strength="strong"/> The output variable is the chance of admit. <break strength="x-strong"/> </speak>