Handling Missing Values and Outliers in Data

Machine Learning    |    Intermediate
  • 13 videos | 1h 22m 6s
  • Earns a Badge
Managing data quality involves mastering techniques to address missing values and outliers to make datasets clean and reliable. This process includes utilizing strategies such as multivariate imputation, outlier detection, and visualization. In this course, learn the fundamentals of data preparation, identify missing values and outliers in datasets, and explore strategies to detect and manage them effectively. Next, discover how to load and prepare data for cleaning, address missing data by dropping records or imputing values, and implement multivariate imputation to achieve more reliable datasets. Finally, explore methods to identify and visualize outliers using box plots and utilize the interquartile range (IQR) and z-score techniques to detect and cap outliers. After taking this course, you will be able to handle missing values and outliers in data.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Describe data preprocessing and feature engineering in machine learning (ml) workflows
    Identify how to handle missing values and outliers in data analysis
    Recognize the impact of outliers on statistical and machine learning (ml) models
    Outline various methods for detecting, identifying, and managing outliers
    Load in a dataset and observe missing values and duplicates
    Perform data cleaning using dropna and the simpleimputer for handling missing values
  • Implement multivariate imputation using the iterativeimputer class
    Detect and cap outliers using interquartile ranges and box plots
    Identify outliers using the interquartile range (iqr) method
    Detect and cap outliers using iqr
    Use the z-score technique to identify and handle outliers
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 58s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 8m 1s
    After completing this video, you will be able to describe data preprocessing and feature engineering in machine learning (ML) workflows. FREE ACCESS
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    3.  Missing Values and Outliers
    8m 12s
    Upon completion of this video, you will be able to identify how to handle missing values and outliers in data analysis. FREE ACCESS
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    4.  Outlier Identification and Analysis
    6m 28s
    Through this video, you will be able to recognize the impact of outliers on statistical and machine learning (ML) models. FREE ACCESS
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    5.  Outlier Detection and Management
    5m 25s
    In this video, we will outline various methods for detecting, identifying, and managing outliers. FREE ACCESS
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    6.  Loading and Preparing Data for Cleaning
    8m 4s
    Learn how to load in a dataset and observe missing values and duplicates. FREE ACCESS
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    7.  Dropping Records and Imputing Values to Address Missing Data
    10m 46s
    In this video, find out how to perform data cleaning using dropna and the SimpleImputer for handling missing values. FREE ACCESS
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    8.  Implementing Multivariate Imputation
    5m 14s
    Discover how to implement multivariate imputation using the IterativeImputer class. FREE ACCESS
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    9.  Detecting and Visualizing Outliers with Box Plots
    7m 28s
    In this video, you will learn how to detect and cap outliers using interquartile ranges and box plots. FREE ACCESS
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    10.  Identifying and Computing Outliers Using IQR
    7m 5s
    Find out how to identify outliers using the interquartile range (IQR) method. FREE ACCESS
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    11.  Capping Outliers with IQR and Evaluating Results
    5m 36s
    In this video, discover how to detect and cap outliers using IQR. FREE ACCESS
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    12.  Using the Z-Score Technique for Outlier Detection and Capping
    7m 23s
    In this video, learn how to use the z-score technique to identify and handle outliers. FREE ACCESS
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    13.  Course Summary
    1m 26s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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