Random Split - To create train valid test dataset using sklearn train test split

sklearn_train_test_split_random.py

import pandas as pd

df = pd.read_csv('/kaggle/input/bluebook-for-bulldozers/TrainAndValid.csv', parse_dates=['saledate'], low_memory=False)

from sklearn.model_selection import train_test_split

# Let's say we want to split the data in 80:10:10 for train:valid:test dataset
train_size=0.8

X = df.drop(columns = ['SalePrice']).copy()
y = df['SalePrice']

# In the first step we will split the data in training and remaining dataset
X_train, X_rem, y_train, y_rem = train_test_split(X,y, train_size=0.8)

# Now since we want the valid and test size to be equal (10% each of overall data). 
# we have to define valid_size=0.5 (that is 50% of remaining data)
test_size = 0.5
X_valid, X_test, y_valid, y_test = train_test_split(X_rem,y_rem, test_size=0.5)

print(X_train.shape), print(y_train.shape)
print(X_valid.shape), print(y_valid.shape)
print(X_test.shape), print(y_test.shape)