Linear Regression in Machine Learning-python-code

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)


In the Next post we see Training and Testing Data

Tags: Linear Regression in Machine Learning-python-code
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