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import matplotlib.style as style

import pandas as pd

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

import plotly.io as pio

import warnings

import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
import seaborn as sns
style.use('fivethirtyeight')
import plotly.express as px
import plotly.graph_objs as go
import plotly.offline as py
import plotly.io as pio
pio.renderers.default = 'iframe'
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import MinMaxScaler, StandardScaler, PowerTransformer, RobustScaler, OrdinalEncoder
from scipy.stats import chi2_contingency
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.neighbors import KNeighborsRegressor
from xgboost import XGBRegressor
from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV

train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')

test['target'] = 0
test['train_or_test'] = 'ts'
train['train_or_test'] = 'tr'
combine = pd.concat([train, test])

combine.info()