text_html_output_1.png
from scipy import stats

from scipy.stats import norm, skew

import matplotlib.pyplot as plt

import os

import pandas as pd

import seaborn as sns

import warnings

import os
import pandas as pd
import numpy as np
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import math
import matplotlib.pyplot as plt
from scipy.stats import skew
import warnings
def ignore_warn(*args, **kwargs):
    pass
warnings.warn = ignore_warn
from scipy import stats
from scipy.stats import norm, skew

training_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
testing_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')

fig, ax = plt.subplots()
ax.scatter(x = training_dataframe['GrLivArea'], y = training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show()

#Deleting outliers
training_dataframe= training_dataframe.drop(training_dataframe[(training_dataframe['GrLivArea']>4000) & (training_dataframe['SalePrice']<300000)].index)

#Check the scatterplot again
fig, ax = plt.subplots()
ax.scatter(training_dataframe['GrLivArea'], training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show()

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