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Data Preprocessing for Machine Learning Using MATLAB!
Introduction to the Course and MATLAB
Introduction to the course (4:24)
Introduction to MATLAB (8:26)
Importing the dataset into MATLAB (7:34)
Handling Missing Values
Source Code and Data
Deletion strategies (8:41)
Using mean and mode (10:42)
Adding special values (6:58)
Class specific mode and mean (12:48)
Random value imputation (14:05)
Dealing with Categorical Variables
Source Code and Data
Categorical data with no order (9:51)
Categorical data with order (6:11)
Frequency encoding (13:04)
Target based encoding (9:20)
Outlier Detection
Source Code and Data
3 sigma rule with deletion strategy (11:27)
3 sigma rule with filling strategy (5:55)
Histograms for outliers (12:56)
Box plots (part 1) (8:18)
Box plots (part 2) (15:41)
LOF (part 1) (6:21)
LOF (part 2) (12:49)
Outliers in categorical variables (8:03)
Feature Scaling and Data Discretization
Source Code and Data
Feature scaling (8:50)
Equal width binning (15:48)
Equal frequency binning (7:35)
Project: Selecting Techniques for your Dataset
Source Code and Data
Selecting the right method (part 1) (16:53)
Selecting the right method (part 2) (10:59)
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Importing the dataset into MATLAB
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