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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.
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.
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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 !
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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]
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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.
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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
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Hi team, wanted to share a good feedback with you, really missed your university, I was training for ML In some reputed university where I cannot mention the name, however I missed you guys, but following you explanation in RUclips instead of unversity recordings, thank you so much for the help .
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 : )
Definitely ! We are glad to have learners like you .Drop your mail id in the comment section for us to share the data sheets or source codes :) Do subscribe our channel and hit that bell icon to never miss an video from our channel .
We are happy that Edureka is helping you learn better ! We are happy to have learners like you :) Please share your mail id to share the data sheets :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thanks for appreciating our efforts ,Pavan. Can you please share your email id with us (it will not be published). We will forward the dataset to your email address.
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!
We do have logistic regression videos that cover everything ruclips.net/video/VCJdg7YBbAQ/видео.html if you need more kindly visit our channel :) dont forget to subscribe for more such videos
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.
Very good explanation mam. I have understood everything. I am having one doubt mam. After checking accuracy i want to find out the result for another values. Means for titanic data if age is 25, female person and 2nd class for this i ant to check y value or for second data income is 25000 and female person then what is y value. So how can i get y value for my variable?
Thanks for the compliment! You can pass the separate validation set instead of the test set with unseen values to get the predictions in the predict().
You have omitted all Nan values. As age contains nan values, removing those values also removes those rows which contain other important values as well. Is it fine to do that?
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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 👍
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.
Excellent explanation. The way you prepare PPTs to explain the concepts is matchless in the industry. keep it up.
This one hour video has given immense clarity and confidence. Thanks team!
I really felt very happy with your explanation, very useful for begginers
Glad it was helpful! Keep learning with us .
You guys are awesome! Explained the concept very clearly and in an understandable way. Thanks a lot!!!
"Over here" great job! 👍🏻
My goodness! How did you get this good at teaching. 👏👏👏
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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 !
You are very very efficient speaker and have delivered great analysis.. thank you
Simply wow. Excellent explanation by you mam. We need professors like u.
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Thank you mam.. got all the concepts...
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
Amazingly defined 👍 Thankyou
After many videos , I got a nice explanation. Kudos to you mam ❤️
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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 !!!
Thankyou ...was able to understand all the concept
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Thanks for your video. It makes life easier.
Glad it helped!
Explanation is tooo good.... Thnkz alot😊
Great explanation 👌 👍 👏 😀
Thank you mam you explained very well love it😀❤️❤️❤️
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very much useful it is. thank you
GREAT EXPLANATION MAM
Fantabulous Presentation Mam!
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You did an excellent job, thank you very much!
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Thanks Edureka....your videos are of high quality ...
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 !
Mem your teaching skill is excellent
You explain point to point and in detail.
#thnx for making this video
well explained , My concepts about logistic regression have cleared . Thank you
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Great session! Thank you :)
One of the best tutorial ever,Mam can you pls share the dataset and source code...Thank you.
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It's so understandable lesson! Thank you.
Thank you mam ,your video very clear ,good help us
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@@edurekaIN OK mam
It's a great tutorial. Take a bow..
Thank you 😊 Glad it helped !!
this is awesome my concept of logistic regression is clear now
Loved the way the lesson is taught.
Great explanation within a short span of time.This lecture has been very helpful.Thank you mam!
Best explanation on logistic regression thank u so much..
A very helpful video.Thank you for the brief tutorial on using Jupyter notebook.
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Thanks Edureka got all the concepts cleared.
Thank you, This is very helpful for my studies.
Thanks Suresh!
Very good explanation for each line of code. Loved it
Thank you mam for vaulable class on logistics regrations and it gives a clear underatanding to me for alogirthms development in ML
Awesome! Really liked it. Live presentations are never this good.
Very much helpful mam🤗
Just a suggestion, if you also share the data being analysed in the videos, it would be a big help to the ones who are watching
Hi Samarth, thanks for the feedback. We will definitely look into your suggestion. Please mention your email id (it will not be published). We will forward the data to your email address.
The real definition of a Queen. Thank you for this.
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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.
Best explanation on regression so far thank u so much
wow very rich in content explained well
Thanks for giving simple short and meaning full information.Thanks
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thank you ma'am.. keep it up
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
Very nice explanation
very useful real case example
Mam i got 💯% accuracy at Titanic Dataset 💪💪💪💪✊✊👍👍👍
Well Explained mam thnx
perfect !! freaking awesome !!...subscribed
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Wonderful explanation mam.
One polite request from my side mam, please could we get the dataset so we can also work on this data set
We are happy that Edureka is helping you learn better ! We are happy to have learners like you :) Please share your mail id to share the data sheets :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Hi team, wanted to share a good feedback with you, really missed your university, I was training for ML In some reputed university where I cannot mention the name, however I missed you guys, but following you explanation in RUclips instead of unversity recordings, thank you so much for the help .
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 : )
Thx u. Very clear instruction
Thanks for Nice lecture .
please send data set list for practices.
Definitely ! We are glad to have learners like you .Drop your mail id in the comment section for us to share the data sheets or source codes :) Do subscribe our channel and hit that bell icon to never miss an video from our channel .
Thanks you madam it very clear cut explanation
Wow. Great explanation
Great explain..from where can I fetch dataset?
We are happy that Edureka is helping you learn better ! We are happy to have learners like you :) Please share your mail id to share the data sheets :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
very well explained ,thank you for such good explanation...
Wonderfull explanation..thanq edurekha 🙂 can u pls share me the datasets plz...
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 ever
Thank You, This tutorial is Very Nice
She is simply wow..Btw can i have the notebook?
Hey Rishi, glad you loved the video. Please do mention your email id(we won't publish it) so that we can mail the files to you. Cheers!
edureka! Thanks..but I did it meanwhile watching the video..thanks again
best explanation of logistic regression
I loved your teaching. Please provide the data set, please. Thanks
Thanks for appreciating our efforts ,Pavan. Can you please share your email id with us (it will not be published). We will forward the dataset to your email address.
Sure. chandrapavan1991@gmail.com. thank you
Hi Pavan can you please send me data set, ajithmail2011@gmail.com
Thank you so much 😍😍
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!
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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good explanation
Titanic Survivors
Accuracy score can be increased to ~84%
Do this,
X_train,X_test,y_train,y_test = train_test_split(X,y, test_size = 0.2, random_state = 33)
You might get error in some cases, so also change this,
model = LogisticRegression(solver='lbfgs',max_iter=10000)
Output
print(classification_report(y_test,y_pred))
precision recall f1-score support
0 0.84 0.91 0.87 111
1 0.83 0.72 0.77 67
accuracy 0.84 178
macro avg 0.83 0.81 0.82 178
weighted avg 0.84 0.84 0.83 178
Excellent presentation.
Thank you.
What is the real practical application of this titanic data set ?
Very well explain. Keep it up Edureka! Team
Thank you... Really helpful.
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 Madam! very good explanation
Wonderful explanation madam
Thank You 😊 Glad it was helpful!! Keep learning with us..
Do you have a video where you did odds and odd ratio for Logistic Regression?
We do have logistic regression videos that cover everything ruclips.net/video/VCJdg7YBbAQ/видео.html if you need more kindly visit our channel :) dont forget to subscribe for more such videos
Nice video..Please provide the data set
Thank you soo much very nice class
Thank u..😇
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.
Thanks a lot, Sister. Keep it up.
helpfull..thnku
Very good explanation mam. I have understood everything. I am having one doubt mam. After checking accuracy i want to find out the result for another values. Means for titanic data if age is 25, female person and 2nd class for this i ant to check y value or for second data income is 25000 and female person then what is y value. So how can i get y value for my variable?
Thanks for the compliment!
You can pass the separate validation set instead of the test set with unseen values to get the predictions in the predict().
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
You have omitted all Nan values. As age contains nan values, removing those values also removes those rows which contain other important values as well. Is it fine to do that?
Hey Dhiraj, Yes, it is absolutely fine to remove NaN values as they do not affect processing in the later stages of dependencies as well.
edureka! Ok.. thanks
Great explanation,pls share me the datasets