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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
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')

train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')

train = train_original.copy()
test = test_original.copy()

def showMissingValues(dataset):
    for col in dataset.columns.tolist():
        print(f' {col} column missing values: {dataset[col].isnull().sum()}')
    print('\n')
print('Train data-------------------------------------')
showMissingValues(train)
print('Validation dataset--------------------------------------')
showMissingValues(test)