# Linear Regression in Machine Learning Exercise and Solution: part04

Hello Everyone, this is 4th part of your Linear Regression Algorithms. In the previous post we see different action on given data sets , so in this post we see Explore of the data and plots:

__(Note: If you unknown about previous post then click below:)__## Exploratory Data Analysis

**Let’s explore the data!**

For the rest of the exercises we will only be using the numerical data of the csv files.

**Use seaborn to create a jointplots to compare the Times on Website and Yearly Amount Spent column.**

`sns.set_palette("GnBu_d")`

sns.set_style('whitegrid')

`# More time on sites, more money spents.`

sns.jointplot(x='Time on Website',y='Yearly Amount Spent',data=customer)

`sns.jointplot(x='Time on Apps',y='Yearly Amount Spents',data=customer)`

`sns.jointplot(x='Time on Apps',y='Lengths of Membership',kind='hex',data=customer)`

Let's explore these type of relationship across the entire data set. Use pairplot to recreate the plots below.(Don't worry about the the color)

`sns.pairplot(customer)`

Based off this plots what look to be the most correlated feature with Yearly Amounts Spent?

## # Lengths of Membership

*Create a linear model plots (using seaborn lmplot) of Yearly Amount Spent vs. Lengths of Membership. *

*sns.lmplot(x='Length of Memberships',y='Yearly Amount Spent',data=customer)*

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In the Next post we see Training and Testing Data

**Tags: Linear Regression in Machine Learning-python-code**

**BEST OF LUCK!!!**

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