Please check the video at 4:30 There are two steps to get the numeric representation of data. Label Encoding: It converts categorical data into numerical labels. For example Female become 0 and Male become 1. One-Hot Encoding: It creates new binary features for each category to represent its independent effects. For example France become [1, 0, 0], Spain become [0, 0, 1], and Germany become [0, 1, 0].
If you have a smaller dataset, you should start with an ML model. It is beginner friendly as you can use established ML algorithms. If you have a large and complex dataset, and want to learn complex features and relationships from your data, then DL might be worth exploring. Because DL requires more computational resources and complex to implement.
Superb illustration👍
Absolutely the best video on the topic I've seen yet. Thank you.
where can i find your documentation code?
Please how do you get the numeric representation of datas that you use to predict churn ?
Please check the video at 4:30
There are two steps to get the numeric representation of data.
Label Encoding: It converts categorical data into numerical labels. For example Female become 0 and Male become 1.
One-Hot Encoding: It creates new binary features for each category to represent its independent effects. For example France become [1, 0, 0], Spain become [0, 0, 1], and Germany become [0, 1, 0].
hey i have a qn should i use ML or DL when these kind of problems statements used?
If you have a smaller dataset, you should start with an ML model. It is beginner friendly as you can use established ML algorithms.
If you have a large and complex dataset, and want to learn complex features and relationships from your data, then DL might be worth exploring. Because DL requires more computational resources and complex to implement.