Is Microsoft excel a good alternative for a quick and approximate linear regression prediction business case? I think yes, but let’s do a…Around 13 years ago, Scikit-learn development started as a part of Google Summer of Code project by David Cournapeau. As time passed Scikit-learn became one of the most famous machine learning library in Python. It offers several classifications, regression and clustering algorithms and its key strength, in my opinion, is seamless integration with Numpy, Pandas and Scipy.
Around 13 years ago, Scikit-learn **development started as a part of **Google Summer of Code project by David Cournapeau. As time passed Scikit-learn became one of the most famous machine learning library in Python. It offers several classifications, regression and clustering algorithms and its key strength, in my opinion, is seamless integration with Numpy, Pandas and Scipy.
In this article, I will compare the prediction accuracy of multiple linear regression of Scikit-learn with excel. Scikit-learnoffers many parameters (known as hyper-parameters of an estimator) to fine-tune the training of the model and increase the accuracy of prediction. In the excel, we do not have much to tune the regression algorithm. For a fair comparison, I will train the sklearn regression model with default parameters.
This comparison aims to learn the prediction accuracy of the linear regression in excel and Scikit-learn. Also, I will touch briefly on the process to perform linear regression in excel.
Sample Data File
For the comparison, we will use historical 100,000 readings of precipitation, minimum temperature, maximum temperature and wind speed, measured several times in a day for 8 years.
We will use the precipitation, minimum temperature and maximum temperature to predict the wind speed. Hence, wind speed is the dependent variable, and other data is the independent variable.
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On this post I’ll focus on supervised problem with continuous valued input. In other words, I’ll trained an algorithm with a dataset compose of known input and output.
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