Logistic Regression in Python | Logistic Regression Example | Machine Learning Algorithms | Edureka
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- Опубликовано: 28 май 2018
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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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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.
Thank you mam.. got all the concepts...
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!!!
God bless you, Thank you so much for this
This one hour video has given immense clarity and confidence. Thanks team!
"Over here" great job! 👍🏻
Great video and a very thorough and clear explanation . Helpful session for the day . Thanks a lot !!!
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.
Thank You, This tutorial is Very Nice
Awesome! Really liked it. Live presentations are never this good.
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
Very good explanation for each line of code. Loved it
Thanks you madam it very clear cut explanation
Thanks Edureka....your videos are of high quality ...
Thank you so much ma'am. Really its a great tutorial.
thank you ma'am.. keep it up
You are very very efficient speaker and have delivered great analysis.. thank you
Mem your teaching skill is excellent
You explain point to point and in detail.
#thnx for making this video
Thanks Edureka got all the concepts cleared.
Best explanation on regression so far thank u so much
Thanks a lot, Sister. Keep it up.
Thank you soo much very nice class
very well explained ,thank you for such good explanation...
Great session! Thank you :)
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 !
Amazingly defined 👍 Thankyou
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.
Thank you, This is very helpful for my studies.
Thanks Suresh!
this is awesome my concept of logistic regression is clear now
Thank you Madam! very good explanation
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!
thanks for video...liked it
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!
Nice video,more way of wrangling the data to view NA :
titanic_data.isnull().any()
very much useful it is. thank you
Thank you... Really helpful.
Wonderful explaination. 👏👏
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!
helpfull..thnku
Best explanation on logistic regression thank u so much..
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
Great explanation,pls share me the datasets
Thanks, really helpful
Nice video..Please provide the data set
You Guys are awesome.
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!
Explanation is tooo good.... Thnkz alot😊
great efforts!! can you share the dataset?
best explanation of logistic regression
I really felt very happy with your explanation, very useful for begginers
Glad it was helpful! Keep learning with us .
Very well explain. Keep it up Edureka! Team
Thank you for such a wonderful lesson!
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 :)
Explained very clear, need to go bit slow.
Wow. Great explanation
wow very rich in content explained well
very useful real case example
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!
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 ruclips.net/video/Pj0neYUp9Tc/видео.html
Wonderfull explanation..thanq edurekha 🙂 can u pls share me the datasets plz...
Thank you so much.
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 : )
Thank you so much 😍😍
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 : )
Great explanation 👌 👍 👏 😀
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 : )
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
GREAT EXPLANATION MAM
Thank you!
hi ,your video is nice ,provide data sets for both the examples that you have discussed..
email id: mohanraogorapalli@gmail.com
Thankyou Soooooo Much Ma'am!!!!!!
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
very good tutorial
Thank u..😇
how to get dataset?
Very much helpful mam🤗
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 : )
Very well explained. The explanations are precise as well as on the point.
Thank you.
p.s: can you please provide the link to the dataset?
Thanks
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 : )
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!
Excellent tutorial.
Thank you for watching our video. Do subscribe, like and share to stay connected with us. Cheers :)
nice explaination
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.
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 : )
Well Explained mam thnx
Very nice explanation
Best explanation ever
mam love you . . .! simply awesome . . .!
Thank you so much! Very helpful!
Good To know our videos are helping you learn better :) Stay connected with us and keep learning ! Do subscribe the channel for more 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.
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