application_vnd.jupyter.stderr_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

train = pd.read_csv('C:\\Users\\sreeram\\Desktop\\pythonfiles\\House-Train.csv')

train.columns

#Correlations with the target variable
a = train.corr()
b = print(a['SalePrice'].sort_values(ascending = False )[:10],'\n')

var = 'GrLivArea'
plt = pd.concat([train['SalePrice'], train[var]], axis=1)

var = 'TotalBsmtSF'
plt = pd.concat([train['SalePrice'], train[var]], axis=1)

var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);

var = 'YearBuilt'
data = pd.concat([train['SalePrice'],train[var]], axis = 1)
f,ax = plt.subplots(figsize = (8,6))
fig = sns.boxplot(x=var, y='SalePrice',data = data)
fig.axis(ymin =0 ,ymax = 800000)

missing = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([missing, percent], axis=1, keys=['missing', 'percent'])
missing_data.head(20)