text_plain_output_2.png application_vnd.jupyter.stderr_output_1.png
import numpy as np
import pandas as pd
import pandas_profiling
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)
import warnings
warnings.filterwarnings('ignore')
from IPython.display import Image
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from sklearn.isotonic import IsotonicRegression
import random
import os
from copy import deepcopy
from functools import partial
from itertools import combinations
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.model_selection import StratifiedKFold, KFold, RepeatedKFold
from sklearn.metrics import roc_auc_score, accuracy_score, mean_squared_error, mean_absolute_error
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA
import seaborn as sns
from sklearn import preprocessing
from category_encoders import OneHotEncoder, OrdinalEncoder, CountEncoder
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import VotingRegressor
import optuna
import xgboost as xgb
import lightgbm as lgb
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from catboost import CatBoost, CatBoostRegressor, CatBoostClassifier
from catboost import Pool
from h2o.automl import H2OAutoML
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))