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This Edureka PPT on Linear Regression Vs Logistic Regression covers the basic concepts of linear and logistic models. The following topics are covered in this session:
Types of Machine Learning
Regression Vs Classification
What is Linear Regression?
What is Logistic Regression?
Linear Regression Use Case
Logistic Regression Use Case
Linear Regression Vs Logistic Regression
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Linear Regression vs Logistic Regression | Edureka
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Agenda
32 41 5
Types of Machine
Learning
Regression Vs
Classification
What is Linear
Regression?
Linear Regression
Use case
What is Logistic
Regression?
6
Logistic Regression
Use case
7
Linear Vs Logistic
Regression
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Types Of Machine Learning
Reinforcement LearningSupervised Learning Unsupervised Learning
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Regression And Classification
Machine Learning
Supervised learning Unsupervised learning Reinforcement learning
Classification Regression
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Regression Vs Classification
Classification Regression
Classification is the task of predicting a discrete class label Regression is the task of predicting a continuous quantity
• In a classification problem data is classified into one of
two or more classes
• A classification problem with two classes is called binary,
more than two classes is called a multi-class
classification
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• A regression problem requires the prediction of a quantity
• A regression problem with multiple input variables is
called a multivariate regression problem
Predicted price
Actual price
Time
Price
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What Is Linear Regression?
Linear Regression is a method to predict dependent variable (Y) based on values of independent
variables (X). It can be used for the cases where we want to predict some continuous quantity.
• Dependent variable (Y):
The response variable who’s value needs to be
predicted.
• Independent variable (X):
The predictor variable used to predict the response
variable.
The following equation is used to represent a linear
regression model:
Y= 𝒃 𝟎 + 𝒃 𝟏 𝒙 + ⅇx
y
independent variable
dependentvariable
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What Is Linear Regression?
Linear Regression is a method to predict dependent variable (Y) based on values of independent
variables (X). It can be used for the cases where we want to predict some continuous quantity.
x
y
independent variable
dependentvariable
Y= 𝒃 𝟎 + 𝒃 𝟏 𝒙 + ⅇ
dependent variable
Y intercept
Slope
independent variable
Error
Y intercept ( 𝑏0)
Error (e)
Slope ( 𝑏1)
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What Is Logistic Regression?
Logistic Regression is a method used to predict a dependent variable, given a set of
independent variables, such that the dependent variable is categorical.
x
y
independent variable
dependentvariable
0
1
• Dependent variable (Y):
The response binary variable holding values like 0 or 1,
Yes or No, A, B or C
• Independent variable (X):
The predictor variable used to predict the response
variable.
The following equation is used to represent a linear
regression model:
𝐘
𝟏 − 𝐘
log = C + B1X1 + B2X2 + ….
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What Is Logistic Regression?
Logistic Regression is a method used to predict a dependent variable, given a set of
independent variables, such that the dependent variable is categorical.
x
y
independent variable
dependentvariable
0
1 𝐘
𝟏 − 𝐘
log = C + B1X1 + B2X2 + ….
• Y is the probability of an event to happen which
you are trying to predict
• x1, x2 are the independent variables which
determine the occurrence of an event i.e. Y
• C is the constant term which will be the probability
of the event happening when no other factors are
considered
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Linear Regression Use Case
To forecast monthly sales by studying the relationship between the monthly e-commerce
sales and the online advertising costs.
Monthly sales
Advertising cost
In 1000s
200
900
450
680
490
300
0.5
5
1.9
3.2
2.0
1.0
200
400
600
800
1000
1.0 2.0 3.0 4.0 5.0
Cost in 1000s
Monthlysales
y
x
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Logistic Regression Use Case
To predict if a student will get admitted to a school based on his CGPA.
Admission CGPA
0
0
0
1
1
1
4.2
5.1
5.5
8.2
9.0
9.1
0
1
2.0 4.0 6.0 8.0 10
CGPA
Admission
y
x
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Linear Regression Vs Logistic Regression
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Linear Regression Vs Logistic Regression
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WebDriver vs. IDE vs. RC
➢ Data Warehouse is like a relational database designed for analytical needs.
➢ It functions on the basis of OLAP (Online Analytical Processing).
➢ It is a central location where consolidated data from multiple locations (databases) are stored.