Data Science Basic | Data Cleaning and Preprocessing by Diljeet Singh | Learn Smarter
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Data Cleaning and Preprocessing

Data cleaning processes are essential for ensuring data accuracy, consistency, and usability. Techniques such as handling missing data, removing duplicates, and detecting outliers play crucial roles in data preprocessing. Moreover, converting data types and normalizing features enhances the performance of analytical models.

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Sections

  • 5

    Data Cleaning And Preprocessing

    This section discusses the importance of data cleaning and preprocessing in preparing raw data for analysis.

    Learning Practice

  • 5.1

    Description

    Learning

  • 5.2

    Learning Objectives

    This section outlines the essential learning objectives of the chapter on data cleaning and preprocessing.

    Learning Practice

  • 5.3

    Why Data Cleaning Matters

    Data cleaning is vital to ensure data quality, which impacts the accuracy and reliability of insights derived from data analysis.

    Learning Practice

  • 5.4

    Handling Missing Data

    This section focuses on techniques for detecting and handling missing data in datasets, ensuring data cleanliness and integrity.

    Learning Practice

  • 5.4.1

    Detecting Missing Values

    This section explains how to identify missing values in datasets using Python, providing tools for accurate data analysis.

    Learning Practice

  • 5.4.2

    Handling Techniques

    This section discusses techniques to handle data quality issues, focusing on missing values, duplicates, data type conversions, and normalization methods.

    Learning Practice

  • 5.5

    Removing Duplicates

    This section focuses on the importance of identifying and removing duplicate entries in data to ensure quality and accuracy.

    Learning Practice

  • 5.6

    Data Type Conversion

    This section discusses the importance of data type conversion for maintaining consistency and efficiency in data processing.

    Learning Practice

  • 5.7

    Outlier Detection & Removal

    This section discusses methods for detecting and removing outliers from datasets to enhance data quality for analysis.

    Learning Practice

  • 5.7.1

    Using Iqr Method

    The IQR method is a statistical technique used to detect and remove outliers based on the interquartile range of a dataset.

    Learning Practice

  • 5.7.2

    Using Z-Score (Optional)

    This section discusses the Z-Score method for outlier detection, providing an efficient way to identify anomalies in datasets.

    Learning Practice

  • 5.8

    Feature Scaling

    Feature scaling techniques like normalization and standardization help prepare numerical data for modeling.

    Learning Practice

  • 5.8.1

    Normalization (Min-Max Scaling)

    Normalization, specifically Min-Max Scaling, adjusts numerical data to fall within a specific range, enhancing model performance.

    Learning Practice

  • 5.8.2

    Standardization (Z-Score Scaling)

    Standardization (Z-score Scaling) transforms data to have a mean of 0 and a standard deviation of 1, facilitating comparisons across different datasets.

    Learning Practice

  • 5.9

    Chapter Summary

    This chapter focuses on the importance of data cleaning and preprocessing to ensure data accuracy and usability in analysis and modeling.

    Learning Practice

References

Chapter 5_ Data Cleaning and Preprocessing.pdf

Class Notes

Memorization

What we have learnt

  • Cleaning data ensures accur...
  • Handle missing data through...
  • Remove duplicates and detec...

Final Test

Revision Tests