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import matplotlib.pyplot as plt

import pandas as pd

import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

import warnings

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.api as sm
from sklearn.linear_model import LinearRegression, Ridge, LogisticRegression
from sklearn.preprocessing import LabelEncoder, PolynomialFeatures
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
from scipy import stats
from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from sklearn.ensemble import RandomForestClassifier
from imblearn.over_sampling import SMOTE
from graphviz import Source
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import confusion_matrix, classification_report
from mlxtend.plotting import plot_confusion_matrix
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
import warnings
warnings.filterwarnings('ignore')
sns.set(style='darkgrid')
plt.style.use('fivethirtyeight')

train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv', index_col=None)
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv', index_col=None)

def dataset_overview(data, col):
    pass

dataset_overview(train, 'satisfaction')