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

This Edureka Video on Logistic Regression in Python will give you basic understanding of Logistic Regression Machine Learning Algorithm with examples. In this video, you will also get to see demo on Logistic Regression using Python. Below are the topics covered in this tutorial:

1:10 What is Regression?
3:22 What is Logistic Regression: What & Why?
8:43 Linear Vs Logistic Regression
10:13 Logistic Regression Use Cases
12:14 Logistic Regression Example Demo in Python

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#Edureka #EdurekaMachineLearning #logisticregression #logisticregressionpython #machinelearningalgorithms

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About the Course

Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR.
During our Python Certification Training, our instructors will help you to:

1. Master the basic and advanced concepts of Python
2. Gain insight into the 'Roles' played by a Machine Learning Engineer
3. Automate data analysis using python
4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
6. Explain Time Series and it’s related concepts
7. Perform Text Mining and Sentimental analysis
8. Gain expertise to handle business in future, living the present
9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience

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Why learn Python?

Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.

[BriteRoy]

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[ShubhamKumar-fyfl]

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[himanshushekharsingh]

Just to clear my concept on logistic regression i searched L R and saw this video. It is perfectly explained by the instructor. Each and every part is well explained. Glad to see this video. A big thumbs up👍 and Thanks.

[sureshkumaratkuri]

Excellent explanation. The way you prepare PPTs to explain the concepts is matchless in the industry. keep it up.

[sriharitha]

I really felt very happy with your explanation, very useful for begginers

[astrovert.ed]

This one hour video has given immense clarity and confidence. Thanks team!

[yaminiharikrishnan]

Thank You, its a very helpful Video. Like to share share 2 points - 1) In Code line # 63 I could not import cross_validation from sklearn library, so I substituted with 'from sklearn.linear_model import LogisticRegression' and then it worked 2) I dropped "Fare" column and it gave a 100 % accuracy on test data !

[poornaacharya]

You guys are awesome! Explained the concept very clearly and in an understandable way. Thanks a lot!!!

[naynadhone]

Thank you mam.. got all the concepts...

[sandeeppanchal]

Hi, presentation is really good. Anybody can understand it easily. Thanks for such wonderful lecture.


Input: Our prediction can go to ~ 82% if we can fill the null values in 'Age' column with average values and can be done by 2 methods.
1) Fill the null values with the value which is the average of all age. (df['Age].mean(). Where df variable name for our dataframe)
2) Fill the null values by taking the average values with respect to column 'Pclass'. Example: If average age of passengers travelling in 1st class is taken and fill the null values with respect to 1st class. Same is done for 2nd and 3rd class. Average age with respect to 'Pclass' can be assumed from the boxplot of seaborn with 'Age' as x and 'Pclass' as y.

Method 2 is better over method 1.

Look at the code to fill the null values in 'Age' with respect to 'Pclass'. (train is the variable name of dataframe)

def impute_age(cols):
Age = cols[0]
Pclass = cols[1]

if pd.isnull(Age):

if Pclass == 1:
return 37

elif Pclass == 2:
return 29

else:
return 24

else:
return Age

train['Age'] = train[['Age', 'Pclass']].apply(impute_age, axis=1)

My prediction is as follows:

Accuracy:
82.02247191011236

Classification Report
precision recall f1-score support

0 0.81 0.93 0.86 163
1 0.85 0.65 0.74 104

micro avg 0.82 0.82 0.82 267
macro avg 0.83 0.79 0.80 267
weighted avg 0.82 0.82 0.81 267

Confusion Matrix:
[[151 12]
[ 36 68]]

Predicted 0 1
Actual
0 151 12
1 36 68

[nehareddy]

Simply wow. Excellent explanation by you mam. We need professors like u.

[hamzahilori]

My goodness! How did you get this good at teaching. 👏👏👏

[shivaaryaprakash]

You are very very efficient speaker and have delivered great analysis.. thank you

[elebs_d]

God bless you, Thank you so much for this

[aadika]

Thanks for your video. It makes life easier.

[shubham_sb]

Thankyou ...was able to understand all the concept

[PushK-yuph]

Great video and a very thorough and clear explanation . Helpful session for the day . Thanks a lot !!!

[ArunKumar-miiq]

After many videos, I got a nice explanation. Kudos to you mam ❤️

[khalidhasan]

It's a great tutorial. Take a bow..

[dikshamakkar]

Explanation is tooo good.... Thnkz alot😊