Building ML Training Sets: Preprocessing Datasets for Linear Regression
Machine Learning
| Beginner
- 7 videos | 50m 12s
- Includes Assessment
- Earns a Badge
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
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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
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2m 40s
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9m 21sDuring this video, you will learn how to use the Pandas library to load a csv file into a dataframe and analyze its contents using Pandas and Matplotlib. FREE ACCESS
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3. Building and Evaluating a Linear Regression Model8m 30sIn this video, you will 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. FREE ACCESS
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4. Scaling and Encoding the Data6m 15sLearn how to apply min-max scaling to the continuous fields and one-hot encoding to the categorical columns of a dataset. FREE ACCESS
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5. Analyzing the Effects of Preprocessing7m 29sAfter completing this video, you will be able to recognize the benefits of scaling and encoding datasets by evaluating the performance of a regression model built with preprocessed data. FREE ACCESS
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6. Standardizing Continuous Data7m 37sIn this video, you will use scikit-learn's StandardScaler on the continuous features of a dataset and compare its effects with that of min-max scaling. FREE ACCESS
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7. Exercise: Preprocessing Data for Regression8m 20sIn this video, find out how to 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. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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