A practical guide to getting started with Machine Learning

In this article, I will show you how to get started with ML using python and tackle a simple regression problem using Support Vector Regression(SVR).The first thing you need to get started is a data set..For this example, we will be using the Boston House price data set which has 506 records, 13 features and a single output(more information on this data set can be found here).Let’s get right to it..If we use a simple train_test_split() and break the data set to training and testing sets such that 80% of the data is used as the training set and 20% as the testing set, the model will be trained using the first 448 records..As majority of the records are in the values range of 200–1500, the model will establish certain patterns using those values and when you test the model using the test set where the majority of the data records are in the value range of 0–100 the model will perform poorly as it was trained using records in a completely different value range.seed = 42X, y = shuffle(X, y, random_state=seed)Setting a seed to a constant value makes sure that you get the same result each time you run your script.. More details

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