I find the iloc code too restrictive, especially if you adjust the data later in the game. Instead I prefer to use code like:
column_names= df.columns
features = column_names[1:]
label = column_names[0]
display(features, label)
so at any point I can ask/query/return df[label] or df[features] without grid numerical references. This way for future iterations I can add or drop a column and only this section needs to be updated.
dependent = df[label].values
independent = df[features].values
What causes the error found input variables inconsistent with samples [7,5000]
Excellent training video. Thank you
If i have 1000 rows in dataset. Then how can select first 200 rows for testing and last 800 rows for training instead of select randomly in splitting?
What's random_state exactly for? Why it's equal to 5
Random state accepts a integer value that defines the random selection of data to be splitted into test and train , it can be any number 1,2,34,5......... Its just defines how random you data will be splitted.
Thank you!
How to split image dataset with xtrain n ytrain
sir for splitting the data set where have to store this data set sir. nice video and easy to understand. give your mail id sir
May I have ur mail-id sir
I APPRECIATE YOUR WORK AND ONLY SOME PEOPLE ARE MAKING THIS QUARANTINE PRODUCTIVE AND ARE GRINDING SO SHOUTOUT TO ALL.THANX FOR VIDEOS.