Data Preprocessing
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
Live Editor Tasks
Functions
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.
Remove Trends
- Remove Linear Trends from Timetable Data
Remove polynomial trend from data using detrend.
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.