row_vector = np.array([1,2,3,4,5,6]) col_vector = row_vector.reshape(6, 1) # 6 rows, 1 column matrix_2x3 = row_vector.reshape(2, 3) # 2 rows, 3 columns print(matrix_2x3)
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Load the Boston Housing dataset. Use train_test_split to hold out 20% of the houses. Train a simple Linear Regression model to predict house prices based on the number of rooms. Print your Root Mean Squared Error. Congratulations—you are a predictive modeler. row_vector = np
By the end of this 90-minute crash course (read time + coding time), you will have built a real-world data pipeline: cleaning a messy dataset, visualizing trends, and running a machine learning model. row_vector = np.array([1
Python alone is slow for math. The magic happens in three libraries.