text_html_output_1.png
from scipy import stats

from scipy.stats import norm, skew # for some statistics

from sklearn.datasets import load_boston

import matplotlib.pyplot as plt

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

import seaborn as sns

from sklearn.datasets import load_boston
load_boston = load_boston()
x = load_boston.data
y = load_boston.target
data = pd.DataFrame(x, columns=load_boston.feature_names)
data['SalePrice'] = y

data.isnull().sum()

plt.tight_layout()

fig, ax = plt.subplots()
ax.scatter(x = data["CRIM"], y = data["SalePrice"])
plt.ylabel("SalesPrice", fontsize = 13)
plt.xlabel("CRIM", fontsize = 13)
plt.show()

fig, ax = plt.subplots()
ax.scatter(x = data["AGE"], y = data["SalePrice"])
plt.ylabel("SalesPrice", fontsize = 13)
plt.xlabel("AGE", fontsize = 13)
plt.show()

from scipy import stats
from scipy.stats import norm, skew
sns.distplot(data['SalePrice'], fit=norm)
mu, sigma = norm.fit(data['SalePrice'])
print('\n mu = {:.2f} and sigma = {:.2f}\n'.format(mu, sigma))
plt.legend(['Normal dist. ($\\mu=$ {:.2f})'.format(mu, sigma)], loc='best')
plt.ylabel('Frequency')
plt.title('Saleprice distribution')
fig = plt.figure()
res = stats.probplot(data['SalePrice'], plot=plt)
plt.show()