import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt

df = pd.read_csv('ad_sales.csv')
df.head()

	ID	TV	Radio	Newspaper	Sales
0	1	230.1	37.8	69.2	22.1
1	2	44.5	39.3	45.1	10.4
2	3	17.2	45.9	69.3	9.3
3	4	151.5	41.3	58.5	18.5
4	5	180.8	12.8	58.4	12.9

#1. Explore the distribution of sales in this dataset

#sns.histplot(data= df, x= 'Sales', kde= True, color= '#d62828',
 #            binwidth= 5)
sns.histplot(data= df, x= 'Sales', kde= True)
plt.show()

#2. What is the relationship between the amount of
#money spent on TV and the amount of money spent on Radio

sns.scatterplot(data= df, x= 'TV', y= 'Radio')
plt.show()

There is no relationship.

#3. What is the relationship between the amount
#of money spent on TV and the Sales?

sns.scatterplot(data= df, x= 'TV', y= 'Sales')
plt.show()

theres a strong positive relationship

#4.  What is the relationship between the
# amount of money spent on Newspaper and the Sales?

sns.scatterplot(data= df, x= 'Newspaper', y= 'Sales')
plt.show()

Theres a weak positive relationship

#convert to HTML

!jupyter nbconvert --to html Bana310_HW_01.ipynb

[NbConvertApp] Converting notebook Bana310_HW_01.ipynb to html
[NbConvertApp] WARNING | Alternative text is missing on 4 image(s).
[NbConvertApp] Writing 415999 bytes to Bana310_HW_01.html