Logistic Regression in Python | Logistic Regression Example | Machine Learning Algorithms | Edureka

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Topics Covered in Today’s Training
What is Regression?
Logistic regression: What and Why?
Linear Vs Logistic Regression
Use- Cases
Demo

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What Is Regression

What is Regression?
Regression Analysis is a predictive modelling technique
It estimates the relationship between a dependent (target)
and an independent variable(predictor)

Logistic Regression:
What And Why

Logistic Regression: What And Why?
Logistic Regression produces results in a binary format which is used to predict the outcome of a
categorical dependent variable. So the outcome should be discrete/ categorical such as:
No Code required to implement RPA
No Downtime.
24/7 up and running
Improved Accuracy
Reduced Costs
0 OR 1
Yes OR No
True OR False
High And Low

Why Not Linear Regression?
Y-axis
X-axis
0
1
Since our value of Y
will be between 0
and 1, the linear line
has to be clipped at
0 and 1.

Why Not Linear Regression?
Y-axis
X-axis
0
1
With this, our resulting
curve cannot be
formulated into a
single formula.
Hence we came up
with Logistic!

Logistic Regression Curve
The Sigmoid “S”
Curve

The Sigmoid “S”
Curve
With this, the
threshold value
indicates the
probability of winning
or losing
0.5
THRESHOLD VALUE
Logistic Regression Curve

The Logistic Regression Equation is derived from the Straight Line Equation
Logistic Regression Equation
Equation of a straight line
Y = C + BIX1 + B2X2 +…. Range is from –(infinity) to (infinity)

Y = C + BIX1 + B2X2 +…. Range is from –(infinity) to (infinity)
Let’s try to reduce the Logistic Regression Equation from Straight Line Equation
Y = C + BIX1 + B2X2 +…. In Logistic equation Y can be only from 0 to 1

Range is from –(infinity) to (infinity)
In Logistic equation Y can be only from 0 to 1
Now , to get the range of Y between 0 and infinity, let’s transform Y
Now, the range is between 0 to infinity
Y = C + BIX1 + B2X2 +….
Y = C + BIX1 + B2X2 +….
Y= 0 then 0
Y= 1 then infinity
Y
1-Y

Range is from –(infinity) to (infinity)
In Logistic equation Y can be only from 0 to 1
Now , to get the range of Y between 0 and infinity, let’s transform Y
Now, the range is between 0 to infinity
Let us transform it further, to get range between –(infinity) and (infinity)
log Y
1-Y
Y = C + BIX1 + B2X2 +…. Final Logistic Regression Equation
Y = C + BIX1 + B2X2 +….
Y = C + BIX1 + B2X2 +….
Y= 0 then 0
Y= 1 then infinity
Y
1-Y

Linear
Logistic

Linear Vs Logistic Regression
Linear Regression Logistic Regression
Continuous variables1
Solves Regression Problems2
Straight line3
Categorical variables1
Solves Classification Problems2
S-Curve3

Logistic Regression:
Use - Cases

Logistic Regression: Use - Cases
Weather
Predictions

Classification
Problems

Determines
Illness

Demo

1
Titanic Data Analysis
Explore titanic dataset and explore about the people, both those who survived and those who did
not. With today's technology, answering questions through data analysis is now easier than ever.
1
What factors made people more likely to
survive the sinking of the Titanic?

Titanic Dataset

Implement Logistic Regression
Analyzing Data
1
2
3
4
5
Collecting Data
Train & Test
Data Wrangling
Accuracy Check

1
2
3
4
5
Collect Data: Import Libraries

1
2
3
4
5
Analyzing Data
Creating different plot to check
relationship between variables

1
2
3
4
5
Data Wrangling
Clean the data by removing
the Nan values and
unnecessary columns in the
dataset

1
2
3
4
5
Train & Test Data
Build the model on the train
data and predict the output on
the test data
logistic = LogisticRegression()
logistic.fit(train_X,train_Y)

1
2
3
4
5
Accuracy Check
Calculate accuracy to check how
accurate your results are.
from sklearn.metrics import accuracy_score
accuracy_score(y_test,predictions)*100

SUV Data Analysis
What factors made people more
interested in buying SUV?
1 A car company has released a new SUV in the market. Using the previous data about the sales of their
SUV’s, they want to predict the category of people who might be interested in buying this.
2

SUV Predictions

Session in a Minute
What is regression? Logistic: What & Why?
Use - Cases
Linear VS Logistic
Demo2: SUV PredictionDemo1: Titanic Analysis
No Code required
to implement RPA
No Downtime.
24/7 up and running
Improved Accuracy
Reduced Costs
0 And 1
Yes And No
True And False
High And Low
Linear Regression Logistic Regression
Continuous variables1
Solves Regression Problems2
Straight line Curve3
Categorical variables1
Solves Classification Problems2
S-Curve3

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Logistic Regression in Python | Logistic Regression Example | Machine Learning Algorithms | Edureka

Logistic Regression in Python | Logistic Regression Example | Machine Learning Algorithms | Edureka

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