Data Preprocessing - MATLAB & Simulink (original) (raw)

Clean, normalize, aggregate, and analyze data

Data preprocessing is the process of transforming raw data into a format that is easier to analyze. This process can include cleaning steps, such as handling missing values or smoothing noisy data. By cleaning, organizing, and summarizing the data, you can identify patterns, make predictions, and inform decision-making.

Apps

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Apply Preprocessing Steps

Live Editor Tasks

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Apply Single Preprocessing Step

Functions

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Clean and Inspect Data

Missing Values

Outliers

Noise Reduction

Local Extrema and Change Points

Sampling

Reshape, Sort, and Resize

Reshape Tables

Sort and Compare Elements

Resize

Normalize and Remove Trends

Normalize

Find and Remove Trends

Bin, Group, and Summarize

Bin

Pivot

Summarize

Topics

Clean Data

• Missing Data in MATLAB
  Handle missing values in data sets.
• Clean Messy and Missing Data in Tables
  Standardize, fill, or remove missing values in tables, and reorganize tables by sorting rows and moving variables.
• Data Smoothing and Outlier Detection
  Eliminate unwanted noise or behavior in data, and find, fill, and remove outliers.
• Clean Messy Data and Locate Extrema Using Live Editor Tasks
  Interactively preprocess data with Live Editor Tasks.

Summarize

• Summarize or Pivot Data in Tables Using Groups
  Interpret data based on common characteristics by creating and visualizing a grouped summary table or pivoted table.
• Perform Calculations by Group in Table
  Specify groups of data in tables and timetables, and perform calculations by group. Choose a function for group calculations using these recommendations.

Featured Examples