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Mastering Numerical Computing with NumPy by Mert Cuhadaroglu, Umit Mert Cakmak

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Univariate linear regression with gradient descent

In this subsection, we will implement univariate linear regression for the Boston housing dataset, which we used for exploratory data analysis in the previous chapter. Before we fit the regression line, let's import the necessary libraries and load the dataset as follows:

In [1]: import numpy as np        import pandas as pd        from sklearn.cross_validation import train_test_split        from sklearn.linear_model import LinearRegression        import matplotlib.pyplot as plt        %matplotlib inlineIn [2]: from sklearn.datasets import load_boston        dataset = load_boston()        samples , label, feature_names = dataset.data, dataset.target, dataset.feature_namesIn [3]: bostondf = pd.DataFrame(dataset.data) bostondf.columns = dataset.feature_names ...

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