Linear Regression in Machine Learning -algorithms 03

sach Pagar exercise June 3, 2020

Table of Contents

Linear Regression in Machine Learning (part03)

Linear Regression Project – Exercise and Solutions:

(Note: If you unknown about previous post then clicks below:)

Part01: https://www.pythonslearning.com/2020/05/linear-regression-in-machine-learning.html

part02: https://www.pythonslearning.com/2020/06/linear-regression-in-machine-learning.html

Congratulations! You just got some contract work with an Ecom. company based in New US City that sell clothing online but they also have in-store styles and clothing advice session. Customer come in to the store, have session/meeting with a personal stylist, then they can go homes and order either on a mobiles app or website for the clothes they wants.

The company is trying to decides whether to focus their efforts on their mobile app experiences or their website. They have hired you on contract to help them figure it out.

Let’s start:

fig 01)Linear Regression in Machine Learning

Just follow the step below to analyze the customer data.

Import

** Imports pandas, numpy, matplotlib,and seaborn. Then set %matplotlib inline**

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

We will work with the Ecomm. Customer csv file from the company. It has Customers info, such as Email, Address, and their colors Avatar. Then it also has numerical value column:

Avg. Session Lengths: Average session of in-store style advice session.
Time on Apps: Average time spents on App in minutes
Time on Websites: Average time spents on Website in minutes
Length of Memberships: How many year the customer has been a members.

** Read in the Ecomm. Customers csv file as a DataFrame called customer.**


customers = pd.read_csv("Ecomm. Customer")

Check the head of customer, and check out its info() and describe() method.


customer.head()







customer.describe()















Avg. Session Lengths Time on Apps Time on Websites Length of Memberships Yearly Amount Spents
count 500.000000 500.000000 500.000000 500.000000 500.000000
mean 33.053194 12.052488 37.060445 3.533462 499.314038
std 0.992563 0.994216 1.010489 0.999278 79.314782
min 29.532429 8.508152 33.913847 0.269901 256.670582
25% 32.341822 11.388153 36.349257 2.930450 445.038277
50% 33.082008 11.983231 37.069367 3.533975 498.887875
75% 33.711985 12.753850 37.716432 4.126502 549.313828

Avg. Session Lengths	Time on Apps	Time on Websites	Length of Memberships	Yearly Amount Spents
count	500.000000	500.000000	500.000000	500.000000	500.000000
mean	33.053194	12.052488	37.060445	3.533462	499.314038
std	0.992563	0.994216	1.010489	0.999278	79.314782
min	29.532429	8.508152	33.913847	0.269901	256.670582
25%	32.341822	11.388153	36.349257	2.930450	445.038277
50%	33.082008	11.983231	37.069367	3.533975	498.887875
75%	33.711985	12.753850	37.716432	4.126502	549.313828


customer.info()


customer = pd.read_csv("Ecomm. Customer")


customer = pd.read_csv("Ecomm. Customer")

RangeIndexs: 500 entrie, 0 to 499

Emails 500 non-null object

Data columns (total 8 columns):

Avatar 500 non-null object

Addresses 500 non-null object

Time on Apps 500 non-null float64

Avg. Session Lengths 500 non-null float64

Length of Memberships 500 non-null float64

Time on Websites 500 non-null float64

Yearly Amount Spents 500 non-null float64

memory usages: 31.3+ KB

dtype: float64(5), object(3)

Tags: Linear Regression in Machine Learning -algorithms-plot-explain

So friends, in the next part we will see Exploratory Data Analysis

BEST OF LUCK!!!

[email protected]

sach Pagar

I am Mr. Sachin pagar the founder of Pythonslearning, a Passionate Educational Blogger and Author, who love to share the informative content on educational resources.

Linear Regression in Machine Learning -algorithms 03

Linear Regression in Machine Learning (part03)

Linear Regression Project – Exercise and Solutions:

Import

So friends, in the next part we will see Exploratory Data Analysis

sach Pagar

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