Encoding categorical data is the process of turning categorical data into integer format so that converted categorical values can be delivered to various models.
This means that if your data contains categorical information, you must convert it to numbers before fitting and evaluating a model. Although a newer technique called learnt embedding may provide a valuable middle ground between these two ways, the two most used techniques are integer encoding and one hot encoding.
One of the benefits of encoding categorical data is that it helps in avoiding missing values.
Another benefit of encoding categorical data is that it helps in reducing the number of features and making the model simpler.
Encoding categorical data is the process of turning categorical data into integer format so that the data with converted categorical values can be delivered to models for prediction and improvement.
Encoding is a method of transforming category variables into numerical values, which may then be easily fitted to a machine learning model.
Learner's Ratings
4.6
Overall Rating
62%
38%
0%
0%
0%
Reviews
R
Rohit Khare
4
What will be the mandatory requirement of configuration of PC for this ML tool
M
Muhammad Fahad Bashir
5
Explained the concept easily
P
Pradeep Kumar Kaushik
5
Please give me iris,csv file.
A
Ankit Malik
4
where is the finaldata.csv
V
Vimal Bhatt
5
great learning plateform kushal sir is really too good
Share a personalized message with your friends.