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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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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]
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 : ) 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 : )
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 : )
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 : )
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.
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 !
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 : )
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!
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 .
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.
We do have logistic regression videos that cover everything ua-cam.com/video/VCJdg7YBbAQ/v-deo.html if you need more kindly visit our channel :) dont forget to subscribe for more such videos
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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!
"Over here" great job! 👍🏻
I really felt very happy with your explanation, very useful for begginers
Glad it was helpful! Keep learning with us .
Amazingly defined 👍 Thankyou
GREAT EXPLANATION MAM
Thank you mam.. got all the concepts...
Great explanation 👌 👍 👏 😀
Fantabulous Presentation Mam!
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very much useful it is. thank you
You guys are awesome! Explained the concept very clearly and in an understandable way. Thanks a lot!!!
Explanation is tooo good.... Thnkz alot😊
You are very very efficient speaker and have delivered great analysis.. thank you
My goodness! How did you get this good at teaching. 👏👏👏
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Thankyou ...was able to understand all the concept
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Great session! Thank you :)
God bless you, Thank you so much for this
Thanks for your video. It makes life easier.
Glad it helped!
It's a great tutorial. Take a bow..
Thank you 😊 Glad it helped !!
Please make tutorials on path planing in robotics and practical implementation
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
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 !
Simply wow. Excellent explanation by you mam. We need professors like u.
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Thanks Edureka....your videos are of high quality ...
Very much helpful mam🤗
Very nice explanation
Loved the way the lesson is taught.
this is awesome my concept of logistic regression is clear now
Well Explained mam thnx
The real definition of a Queen. Thank you for this.
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It's so understandable lesson! Thank you.
What is the real practical application of this titanic data set ?
wow very rich in content explained well
After many videos , I got a nice explanation. Kudos to you mam ❤️
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very useful real case example
Best explanation on logistic regression thank u so much..
good explanation
Thanks Edureka got all the concepts cleared.
Wow. Great explanation
Thank you mam you explained very well love it😀❤️❤️❤️
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Mam i got 💯% accuracy at Titanic Dataset 💪💪💪💪✊✊👍👍👍
You did an excellent job, thank you very much!
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Best explanation ever
Great video and a very thorough and clear explanation . Helpful session for the day . Thanks a lot !!!
thank you ma'am.. keep it up
Very good explanation for each line of code. Loved it
Best explanation on regression so far thank u so much
best explanation of logistic regression
Excellent presentation.
Thank you.
Thank you... Really helpful.
Beautiful
Thank you so much 😍😍
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
well explained , My concepts about logistic regression have cleared . Thank you
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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.
Thx u. Very clear instruction
Thank you mam ,your video very clear ,good help us
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@@edurekaIN OK mam
Thankyou Soooooo Much Ma'am!!!!!!
Nice video..Please provide the data set
Thank u..😇
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 !
helpfull..thnku
Great explain..from where can I fetch dataset?
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Wonderful explanation madam
Thank You 😊 Glad it was helpful!! Keep learning with us..
Mem your teaching skill is excellent
You explain point to point and in detail.
#thnx for making this video
Thank you, This is very helpful for my studies.
Thanks Suresh!
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!
great video
Awesome! Really liked it. Live presentations are never this good.
Thanks you madam it very clear cut explanation
perfect !! freaking awesome !!...subscribed
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Very well explain. Keep it up Edureka! Team
Can you please provide data sets as well
Thank you soo much very nice class
Thanks for Nice lecture .
please send data set list for practices.
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Thanks for giving simple short and meaning full information.Thanks
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A very helpful video.Thank you for the brief tutorial on using Jupyter notebook.
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Thank You, This tutorial is Very Nice
Great explanation,pls share me the datasets
Thank you Madam! very good explanation
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.
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 :)
Thank you so much.
You Guys are awesome.
excellent
Great explanation within a short span of time.This lecture has been very helpful.Thank you 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.
Do you have a video where you did odds and odd ratio for Logistic Regression?
We do have logistic regression videos that cover everything ua-cam.com/video/VCJdg7YBbAQ/v-deo.html if you need more kindly visit our channel :) dont forget to subscribe for more such videos
very well explained ,thank you for such good explanation...
Thank you mam for vaulable class on logistics regrations and it gives a clear underatanding to me for alogirthms development in ML
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!
Thanks a lot, Sister. Keep it up.
well explained
Thank you for such a wonderful lesson!
Do we need to install the sklearn module using pip ?
Hi Sayan, you will use pip install scikit-learn on your python shell.
Thanks