Most of the time data scientists tend to measure the accuracy of the model with the model performance which may or may not give accurate results based on data. Here we will be looking at a few other techniques using which we can compute model performance.
In this post, we will be learning about techniques to generate supervised learning regression models. We will be exploring Linear Regression and Regularized Regression. We will be also looking at various types of Regularized Regression: Ridge Regression and Lasso Regression.
In this post, we will be looking at techniques and k-nearest neighbor algorithm which is used to solve the Supervised Learning Classification model. We will be also looking at the technique to measure the accuracy of the model.
It's very important to visualize data before drawing any conclusions from it. In this post, you will learn how to draw great visualizations from the data and how to use the seaborn library to draw out of box visualizations.
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