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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.
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 !
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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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 : )
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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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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 Please mention your email id (it will not be published).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.
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 : )
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SUV Prediction Instead of removing the gender column, you can include that in the model to increase the accuracy to ~90%. For that just do label encoding for column in data.columns: if data[column].dtype == np.number: continue data[column] = LabelEncoder().fit_transform(data[column]) change this too X_train,X_test,y_train,y_test = train_test_split(X,y, test_size = 0.2, random_state = 30) model = LogisticRegression(solver = 'lbfgs',max_iter = 10000) Output 0.9
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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 UA-cam 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 : )
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 : )
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 : )
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
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.
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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.
I really felt very happy with your explanation, very useful for begginers
Glad it was helpful! Keep learning with us .
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!
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 !
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
"Over here" great job! 👍🏻
Fantabulous Presentation Mam!
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You guys are awesome! Explained the concept very clearly and in an understandable way. Thanks a lot!!!
Simply wow. Excellent explanation by you mam. We need professors like u.
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GREAT EXPLANATION MAM
It's a great tutorial. Take a bow..
Thank you 😊 Glad it helped !!
You are very very efficient speaker and have delivered great analysis.. thank you
Thank you mam.. got all the concepts...
My goodness! How did you get this good at teaching. 👏👏👏
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Please make tutorials on path planing in robotics and practical implementation
Thanks for your video. It makes life easier.
Glad it helped!
The real definition of a Queen. Thank you for this.
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Amazingly defined 👍 Thankyou
Thankyou ...was able to understand all the concept
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After many videos , I got a nice explanation. Kudos to you mam ❤️
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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.
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 !
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.
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
very much useful it is. thank you
this is awesome my concept of logistic regression is clear now
God bless you, Thank you so much for this
Explanation is tooo good.... Thnkz alot😊
Great video and a very thorough and clear explanation . Helpful session for the day . Thanks a lot !!!
well explained , My concepts about logistic regression have cleared . Thank you
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Great session! Thank you :)
Mam i got 💯% accuracy at Titanic Dataset 💪💪💪💪✊✊👍👍👍
good explanation
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!
Thanks Edureka....your videos are of high quality ...
Best explanation on logistic regression thank u so much..
Very nice explanation
wow very rich in content explained well
Great explanation 👌 👍 👏 😀
You did an excellent job, thank you very much!
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Well Explained mam thnx
very useful real case example
Thank you mam you explained very well love it😀❤️❤️❤️
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 : )
SUV Prediction
Instead of removing the gender column, you can include that in the model to increase the accuracy to ~90%.
For that just do label encoding
for column in data.columns:
if data[column].dtype == np.number:
continue
data[column] = LabelEncoder().fit_transform(data[column])
change this too
X_train,X_test,y_train,y_test = train_test_split(X,y, test_size = 0.2, random_state = 30)
model = LogisticRegression(solver = 'lbfgs',max_iter = 10000)
Output
0.9
Thank you mam ,your video very clear ,good help us
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@@edurekaIN OK mam
Thanks Edureka got all the concepts cleared.
Loved the way the lesson is taught.
It's so understandable lesson! Thank you.
best explanation of logistic regression
Thankyou Soooooo Much Ma'am!!!!!!
Wow. Great explanation
Best explanation ever
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 UA-cam 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 : )
Very much helpful mam🤗
Mem your teaching skill is excellent
You explain point to point and in detail.
#thnx for making this video
Wonderful explanation madam
Thank You 😊 Glad it was helpful!! Keep learning with us..
A very helpful video.Thank you for the brief tutorial on using Jupyter notebook.
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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!
Beautiful
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
thank you ma'am.. keep it up
Thank you, This is very helpful for my studies.
Thanks Suresh!
Best explanation on regression so far thank u so much
Excellent presentation.
Thank you.
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 : )
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.
Very good explanation for each line of code. Loved it
Nice video..Please provide the data set
great video
Thank you... Really helpful.
Wonderfull explanation..thanq edurekha 🙂 can u pls share me the datasets plz...
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 .
excellent
helpfull..thnku
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 : )
Can you please provide data sets as well
Thank u..😇
Thank you mam for vaulable class on logistics regrations and it gives a clear underatanding to me for alogirthms development in ML
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
What is the real practical application of this titanic data set ?
Thank you so much 😍😍
Thanks you madam it very clear cut explanation
very well explained ,thank you for such good explanation...
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 :)
Very well explain. Keep it up Edureka! Team
Thank you so much.
Great explanation within a short span of time.This lecture has been very helpful.Thank you mam!
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 Madam! very good explanation
well explained
Thx u. Very clear instruction
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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Great explanation,pls share me the datasets
Thank you soo much very nice class
perfect !! freaking awesome !!...subscribed
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yes
Thanks