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A collection of datasets of ML problem solving. Contribute to selva86/datasets development by creating an account on GitHub.
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The Boston Housing Dataset. Objectives. Analyse and explore the Boston house price data. Split the data for training and testing. Run a Multivariable Regression. Evaluate how the model's coefficients and residuals. Use data transformation to improve the model performance. Use the model to estimate a property price. Import Statements. In [2]:
Explore and run machine learning code with Kaggle Notebooks | Using data from Boston House Prices
Step 1: Obtain data. The Boston housing dataset is built into scikit-learn, so we can import it easily, as follows. [ ] from sklearn.datasets import load_boston. boston = load_boston()
This dataset contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It was obtained from the StatLib archive ( http://lib.stat.cmu.edu/datasets/boston ), and has been used extensively throughout the literature to benchmark algorithms.
