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import numpy as np
import pandas as pd
from scipy.stats import norm, randint
from math import ceil
import time
import os
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
import seaborn as sns
from catboost import CatBoostRegressor
from sklearn.compose import ColumnTransformer
from sklearn.datasets import make_regression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier, GradientBoostingClassifier, GradientBoostingRegressor, RandomForestClassifier, RandomForestRegressor, VotingClassifier
from sklearn.feature_selection import mutual_info_regression
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression, Perceptron, SGDClassifier, LogisticRegression, PassiveAggressiveClassifier, RidgeClassifierCV, Ridge
from sklearn.metrics import accuracy_score, auc, classification_report, confusion_matrix, mean_squared_error, precision_score, recall_score, roc_curve
from sklearn.metrics import mean_squared_error as MSE
from sklearn.model_selection import cross_val_score, cross_val_predict, cross_validate, train_test_split, GridSearchCV, KFold, RepeatedKFold, learning_curve, RandomizedSearchCV, StratifiedKFold
from sklearn.multiclass import OneVsRestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
from sklearn.svm import SVC, LinearSVC, SVR
from sklearn.tree import DecisionTreeClassifier
from sklearn import ensemble, linear_model, neighbors, svm, tree, model_selection, preprocessing
from sklearn import utils
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
import lightgbm as lgbm
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))