# Building ML Training Sets: Preprocessing Datasets for Linear Regression

Machine Learning    |    Beginner
• 7 Videos | 50m 12s
• Includes Assessment
• Earns a Badge
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This 7-video course helps learners discover how to implement machine learning scaling techniques such as standardizing and min-max scaling on continuous data and one-hot encoding on categorical features to improve performance of linear regression models. In the first tutorial, you will use Pandas library to load a CSV file into a data frame and analyze its contents by using Pandas and Matplotlib. You will then learn how to create a linear regression model with scikit-learn to predict the sale price of a house and evaluate this model by using metrics such as mean squared error and r-square. Next, learners will examine the application of min-max scaling on continuous fields and one-hot encoding on the categorical columns of a data set. Then analyze effects of preprocessing by recognizing benefits of scaling and encoding data sets by evaluating the performance of a regression model built with preprocessed data. Also, learn how to use scikit-learn's StandardScaler on a data set's continuous features and compare its effects with that of min-max scaling. The concluding exercise involves preprocessing data for regression.

## WHAT YOU WILL LEARN

• 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

## IN THIS COURSE

• 1.
Course Overview
• 2.
• 3.
Building and Evaluating a Linear Regression Model
• 4.
Scaling and Encoding the Data
• 5.
Analyzing the Effects of Preprocessing
• 6.
Standardizing Continuous Data
• 7.
Exercise: Preprocessing Data for Regression

## EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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