Discover how to implement scaling techniques such as standardizing and min-max scaling on continuous data and one-hot encoding on categorical features to improve the performance of linear regression models.
Building ML Training Sets: Preprocessing Datasets for Linear Regression
use the Pandas library to load a csv file into a dataframe and analyze its contents using Pandas and Matplotlib
create a linear regression model using scikit-learn to predict the sale price of a house and evaluate this model using metrics such as mean squared error and r-square
apply min-max scaling on the continuous fields and one-hot encoding on the categorical columns of a dataset
recognize the benefits of scaling and encoding datasets by evaluating the performance of a regression model built with preprocessed data
use scikit-learn's StandardScaler on the continuous features of a dataset and compare its effects with that of min-max scaling
identify the characteristics of the StandardScaler, encode a feature column which contains certain values, recall two metrics used to evaluate regression models, and enumerate the details conveyed in a Boxplot