image_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)

pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 100)

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

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

#box plot overallqual/saleprice
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=(16, 8))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
plt.xticks(rotation=90);

sns.set(font_scale=1)
correlation_train = train.corr()

train.corr()