image_output_1.png
import pandas as pd

import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn import pipeline
from sklearn import model_selection
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from matplotlib import pyplot as plt
plt.rcParams.update({'figure.figsize': (8.0, 6.0)})
plt.rcParams.update({'font.size': 11})

train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)

sum(train.isnull().sum())

train['target'].hist()
plt.title('Target value distribution')
plt.show()