Building ML Training Sets: Preprocessing Datasets for Linear Regression

Machine Learning
  • 7 Videos | 52m 42s
  • Includes Assessment
  • Earns a Badge
Likes 26 Likes 26
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

  • Playable
    1. 
    Course Overview
    2m 40s
    UP NEXT
  • Playable
    2. 
    Loading and Analyzing a Dataset
    9m 21s
  • Locked
    3. 
    Building and Evaluating a Linear Regression Model
    8m 30s
    FREE ACCESS
  • Locked
    4. 
    Scaling and Encoding the Data
    6m 15s
    FREE ACCESS
  • Locked
    5. 
    Analyzing the Effects of Preprocessing
    7m 29s
    FREE ACCESS
  • Locked
    6. 
    Standardizing Continuous Data
    7m 37s
    FREE ACCESS
  • Locked
    7. 
    Exercise: Preprocessing Data for Regression
    8m 20s
    FREE ACCESS

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