text_plain_output_1.png
from sklearn.model_selection import train_test_split

import pandas as pd

import pandas as pd
from sklearn.model_selection import train_test_split
df = pd.read_csv('../input/30-days-of-ml/train.csv')
test_df = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_df = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
y = df.target
X = df.drop(['target'], axis=1)
X_train_full, X_valid_full, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)
categorical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 5 and X_train_full[cname].dtype == 'object']
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()

[cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 5 and X_train_full[cname].dtype == 'object']