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import pandas as pd # data processing

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import missingno as msno
import seaborn as sns
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict
from sklearn import metrics
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
dataset = pd.read_csv('../input/train.csv')

dataset.isnull().sum()

dataset = dataset.drop(['Id', 'LotFrontage', 'Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1)
len(dataset.columns)