Logistic Regression in Python | Logistic Regression Example | Machine Learning Algorithms | Edureka
Вставка
- Опубліковано 28 тра 2018
- 🔥 Python Data Science Training (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): www.edureka.co/data-science-p...
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
Subscribe to our channel to get video updates. Hit the subscribe button above.
Machine Learning Tutorial Playlist: goo.gl/UxjTxm
PG in Artificial Intelligence and Machine Learning with NIT Warangal : www.edureka.co/post-graduate/...
#Edureka #EdurekaMachineLearning #logisticregression #logisticregressionpython #machinelearningalgorithms
- - - - - - - - - - - - - - - - -
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
- - - - - - - - - - - - - - - - - - -
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.
For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free).
Instagram: / edureka_learning
Facebook: / edurekain
Twitter: / edurekain
LinkedIn: / edureka
Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Python Machine Learning Course curriculum, Visit our Website: bit.ly/2OpzQWw
Thank you so much
It's an awesome explanation, Thank you very much, Please share the source code & datasets to my mail id : rkamakhya@gmail.com
Shrey
1 second ago
hi what if the labels , dependent variable is 7 and 8 do you have to change it to 0- and 1 or do i keep it as it is to perform logistic regression pleas reply asap.
Hi Shrey, it has to be dichotomous. So if there are only two categories, you can transform the labels. Hope that solves your query.
How do you speak so flawlessly without fumbling or pausing even for once. Hats off.
In the world full of greed no one is providing knowledge for free. Edureka you are doing great job 👍
Excellent explanation. The way you prepare PPTs to explain the concepts is matchless in the industry. keep it up.
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.
You guys are awesome! Explained the concept very clearly and in an understandable way. Thanks a lot!!!
This one hour video has given immense clarity and confidence. Thanks team!
Thank you mam.. got all the concepts...
God bless you, Thank you so much for this
Great video and a very thorough and clear explanation . Helpful session for the day . Thanks a lot !!!
"Over here" great job! 👍🏻
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 !
Great explanation within a short span of time.This lecture has been very helpful.Thank you mam!
It's so understandable lesson! Thank you.
Loved the way the lesson is taught.
Awesome! Really liked it. Live presentations are never this good.
You are very very efficient speaker and have delivered great analysis.. thank you
Thank You, This tutorial is Very Nice
Very good explanation for each line of code. Loved it
Thx u. Very clear instruction
Thank you mam for vaulable class on logistics regrations and it gives a clear underatanding to me for alogirthms development in ML
Mem your teaching skill is excellent
You explain point to point and in detail.
#thnx for making this video
Thanks Edureka....your videos are of high quality ...
Thanks Edureka got all the concepts cleared.
Great session! Thank you :)
Thanks you madam it very clear cut explanation
Best explanation on regression so far thank u so much
Amazingly defined 👍 Thankyou
thank you ma'am.. keep it up
Thank you so much ma'am. Really its a great tutorial.
very clearly explained.Hats off Mam
The video is very nice. The way our concepts are getting cleared. Please give us the link to download the notebook which you created as titanic.
Thanks a lot, Sister. Keep it up.
very much useful it is. thank you
this is awesome my concept of logistic regression is clear now
Thank you, This is very helpful for my studies.
Thanks Suresh!
very well explained ,thank you for such good explanation...
Thank you soo much very nice class
A very helpful video.Thank you for the brief tutorial on using Jupyter notebook.
Hi Aditya, thank you for watching our video. Do subscribe, like and share to stay connected with us. Cheers!
I really felt very happy with your explanation, very useful for begginers
Glad it was helpful! Keep learning with us .
Best explanation on logistic regression thank u so much..
Thank you Madam! very good explanation
Thank you... Really helpful.
Thanks, really helpful
Explanation is tooo good.... Thnkz alot😊
thanks for video...liked it
Hi ,
your work is very help full and Thank you. But I was wandering how I can do a prediction for new data set which is not labeled (0 and 1) by using my trained machine and store it to excel.
Hey Niguss, please do check this link to know more. www.jmp.com/support/downloads/pdf/jmp902/modeling_and_multivariate_methods.pdf
Wonderful explaination. 👏👏
perfect !! freaking awesome !!...subscribed
Hey Matitiude, thanks for subscribing! We are glad you loved the video. Do take a look at our other videos too and stay tuned for future updates. Cheers!
Nice video,more way of wrangling the data to view NA :
titanic_data.isnull().any()
helpfull..thnku
Thankyou ...was able to understand all the concept
Thank you so much for the review ,we appreciate your efforts : ) We are glad that you have enjoyed your learning experience with us .Thank You for being a part of our Edureka team : ) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
well explained , My concepts about logistic regression have cleared . Thank you
Hey Bilal, we are glad you feel this way. Do subscribe and hit the bell icon to never miss an update from us in the future. Cheers!
GREAT EXPLANATION MAM
Nice video..Please provide the data set
Simply wow. Excellent explanation by you mam. We need professors like u.
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
One of the best videos in detailed.thanks a lot
Hey Mohammad, thanks for the compliment. We are glad you loved the video. Do subscribe and hit the bell icon to never miss an update from us in the future. Cheers!
Great explanation,pls share me the datasets
You Guys are awesome.
Very well explain. Keep it up Edureka! Team
I really like ur explanation mam!! I have got answers for so many doubts with ur explanation. Can u please tell me where to find this excellent notes?? Want more videos on ML😊
Hi Yashwanth, Thanks for the compliment. We are so glad to hear that you liked our videos. You can always refer to the Machine Learning Playlist of Edureka for more such helpful videos. Here's a link to the playlist ua-cam.com/video/Pj0neYUp9Tc/v-deo.html
wow very rich in content explained well
Hello Can you also make a video on how to plot these predicted values.
Hey Vivek, we will definitely look into your suggestions. We update our channel regularly, stay tuned and never miss out on our updates. Cheers :)
best explanation of logistic regression
Wow. Great explanation
very useful real case example
great efforts!! can you share the dataset?
Thanks for giving simple short and meaning full information.Thanks
Hey Raja, Thank you for appreciating our efforts. We are glad you loved the video. Do subscribe, like and share to stay connected with us!
Great explanation 👌 👍 👏 😀
You did an excellent job, thank you very much!
You're welcome 😊 Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
very good explanation
After many videos , I got a nice explanation. Kudos to you mam ❤️
We are super happy that Edureka is helping you learn better. Your support means a lot to us and it motivated us to create even better learning content and courses experience for you . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thank you mam ,your video very clear ,good help us
Thanks for the compliment Yasmin, we are glad you loved the video. Do subscribe to the channel and hit the bell icon to never miss an update from us in the future. Cheers!
@@edurekaIN OK mam
Thank you so much.
Wonderfull explanation..thanq edurekha 🙂 can u pls share me the datasets plz...
hi ,your video is nice ,provide data sets for both the examples that you have discussed..
email id: mohanraogorapalli@gmail.com
My goodness! How did you get this good at teaching. 👏👏👏
You're welcome 😊 Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thank you for such a wonderful lesson!
One of the best tutorial ever,Mam can you pls share the dataset and source code...Thank you.
Hey Kamlesh, we are glad you loved the video. Do mention your email ID over here and we will send the files to you. Cheers!
Explained very clear, need to go bit slow.
Thank you!
Well Explained mam thnx
Please make tutorials on path planing in robotics and practical implementation
Thank you so much 😍😍
Very much helpful mam🤗
Very nice explanation
Thanks for your video. It makes life easier.
Glad it helped!
why you have used the Standardscalar function in the SUV model , what is the actual use of it ?
Hi Anshika, Scalers are used to scale the values of predictor variables along the same range in order to avoid biasness.
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
good explanation
Thank you for the clear explanation. Can you please provide the datasets and the python notebook used in the video?
Please mention your email id (it will not be published). We will forward the dataset to your email address.
@@edurekaIN my email id is myskillcentral@gmail.com
Thankyou Soooooo Much Ma'am!!!!!!
Thank u..😇
Thanks
can you provide dataset along with tutorial ? or link to get it? on kaggle many datasets are there with 'titanic' name
Hi ! Good to know that our videos are helping you to learn better 😊 Please share your mail id to share the data sheets, We’ll update you soon . Do subscribe the channel for more updates.
Tremendous work with this presentation and project.
Thank you for your review : ) We are glad that you found our videos /contents useful . We are also trying our best to further fulfill your requirements and enhance your expirence :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Best explanation ever
Outstanding explanation. I am pursuing AI Silver from Pixel Tests but your way of explanation is by far the best one. Thanks for sharing your knowledge. Sharing is caring indeed.
We are very glad to hear that your a learning well with our contents 😊 continue to learn with us and don't forget to subscribe our channel so that you don't miss any updates !
It was a good video in titanic dataset, mean should be taken for age column instead of dropping na. Overall, the video was good and nice explanation.
Thank you for appreciating our efforts. We are glad you loved the video. Do subscribe to our channel and stay connected with us.
very good tutorial