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Can I just say how helpful you have been by making everything look so simple on python, there's other youtubers who just presume we are all qualified Data Scientists and know how to always write Machine Learning functions from scratch. You on the other hand teach us the basics of each ML topic and teach us how to use implement ML in datasets SIMPLY!!!!
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@@SimplilearnOfficial Thank you for the reply Simplilearn! I have a quick question, in the confusion matrix you referenced 'mat.T', surely it should just be 'mat'? If it is mat.T, what does the T actually represent? I have looked at the SNS.heatmap condition breakdown on their website but I don't see any information about a .T Any help would be appreciated! Kind regards, Shimmy
@ 13:07 min in the slide of calculation P(B|A) = P(WeekDay | Buy) is wrong. It should be 9/24 not 2/6. 2/6 is P(weekDay | No Buy). Is my understanding correct?
16:27 Result is: ≈0.02 17:16 Result is: ≈0.98 19:15 Then sum of probabilities is: ≈1 (AND NOT 1.164, because sum of probabilities of two related events equals 1) Likelihood of Purchase: 0.98 / 1 = 98% Likelihood of NO Purchase: 0.02 / 1 = 2% And then as 98% > 2% we can conclude that customer will buy on a holiday with discount and free delivery.
I must say your explanation is excellent compared to others. You’re to the point and not unnecessarily stretching, as I’ve seen on other tutorials. Also Please help me with the data set
Hey Dean, thank you for appreciating our work. We are glad to have helped. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Machine Learning is the Future and yours can begin today. Comment below with you email to get our latest Machine Learning Career Guide. Let your journey begin. Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you. Also, if you would like to have the dataset for implementing Naive Bayes Classifier in Python, please comment below and we will get back to you. Thanks for watching the video. Cheers!
Heyy thank you so much. I am doing project called order Prediction my dataset has following columns count year month day hour working_day weekend_day public_holiday then how should I apply naive bayes on it?
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Hello @ Simplilearn! I have become glued to your videos, please can you kindly help with the dataset too to this email? xxxxxxxxx - It will be greatly appreciated. Thanks in anticipation.
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1. For the first problem (12:00 into the lesson), P(No Buy | Weekday) can also be calculated by seeing that there are 11 visits on the weekday and 2 of them are No Buy. Therefore the answer is 2/11. Which does match the answer via Bayes (if the Bayes calculation does not round the fractions) 2. On the example using Holiday, Discount = Yes and Delivery = Yes, I was surprised that P(No Buy | B) + P(Buy | B) does not equal 1. Given that these are the same condition isn't the probability of Buy or No Buy 100% ? 3. P(A|B) When you doing the classification of articles ... is it true that the likelyhood tables are - The article types as the row headers and the key words as the column headers ... so there would be one very large likelyhood table.
Hello Altaf, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Hello. First of all thanks for the video. Video contains at least two mistakes. One is typo. Other is a logical error which is at 17:09. P(A|B,C,D) (where A ∈ {True,False}) is the question. If P(A=True|B,C,D) = p, then P(A=False|B,C,D) = 1-p because sample space of A contains only "True" or "False". Am I wrong? It is probably calculation error. The typo is at 13:10 (P(B|A)=2/6=0.375), it should be 9/24 instead of 2/6. It is left from the previous slide. That's all, have a good day!
at 19:47 timestamp, it is mentioned as sum of probabilities = 0.986+0.178 = 1.164. but, probability can range between (0..1). Can you please explain how sum of probabilities can exceed more than 1?
Hi, Simplilearn provides online training across the world. We would be happy to help you regarding this. Please visit us at www.simplilearn.com and drop us a query and we will get back to you! Thanks!
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Nice and useful! Just curious to ask a question. As far as I understand, in the shopping use case, we used three features (Day, free delivery & discount). And, in the text categorization use case, we used one feature (text containing email content). Is that really treated as one feature here? Or, will the naive bayes algorithm, internally creates multiple features for the given input (text containing email)?
Our feature in the use case of text categorization is "each individual word". Here, we train the model to predict the probability of this particular work being in any of the different categories. The final probability is derived by calculating the overall conditional probability of that particular combination of words (just like we found the probability of different combinations of day, free delivery and discount).
I don't know if it's just me but i think it would've really improved the tutorial if you at least had a mouse pointer pointing where you are explaining. Or the text clould've appeared progressively. I think it makes it hard to follow the tutorial. My eyes just don't know where to look at.
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@@SimplilearnOfficial I'm just having a little bit of problem with the dataset, do you have a video where you show how to do them and how you assign the right tags to the training group?
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You took mat variable for confusion matrix, can you tell me what is the difference between mat and mat.T??? (mat and mat.T both are printing different tables)
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Hi Simplilearn, It was really a nice tutorial and the example of Shopping was superbly explained. I have one doubt regarding code, when you were providing some different input like "Presedent of India", then you were getting output(lable) correctly. How was that working?
This works on the principle of text categorization. We have used Naive Bayes to achieve this in this video, though other algorithms could also be used for the same purpose.
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I have twitter text data around in millions , that i fetched from twitter based on the terms ' Coronavirus'. 'COVID-19', Now i put some labels manually to approximately 2000 posts, Now can i use this algorithm to classify the remaining textual data based on my labels that i gave? PS: I classified the data into eight textual categories! And also for text data classification which algorithm is best ?
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At 12:56 you say 2/6 equals 0.33, at 13:12 you say 2/6 equals 0.375. P(Weekday | No buy) is truly 2/6, but P(Weekday | Buy) is the opposite, 9/24. At 11:05 what? You WRITE P(Weekday), but SAY the probability of making a purchase on a weekend, which would be P(Buy | Weekend). Then you say that the P(Buy | Weekend) is 11/30, but it is 7/24.
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Thanks for the video. I couldn't catch (for the purchase&no purchase example) why the sum of probability of a purchase and probability of no purchase doesn't make 1. When B = (Day=Holiday , Discount=Yes , Free Delivery = Yes) , there are only 2 possible results , buy or no buy. So the sum of the probabilities should equal 1 , isn't it?
@@SimplilearnOfficial There is a calculation mistake @ 15:35 . I think your intuition is wrong, however, I'm not sure as I am still very inexperienced in the field(student). However there is a normalization probability in Bayes(denominator).
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Can I just say how helpful you have been by making everything look so simple on python, there's other youtubers who just presume we are all qualified Data Scientists and know how to always write Machine Learning functions from scratch.
You on the other hand teach us the basics of each ML topic and teach us how to use implement ML in datasets SIMPLY!!!!
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!
@@SimplilearnOfficial Thank you for the reply Simplilearn!
I have a quick question, in the confusion matrix you referenced 'mat.T', surely it should just be 'mat'? If it is mat.T, what does the T actually represent? I have looked at the SNS.heatmap condition breakdown on their website but I don't see any information about a .T
Any help would be appreciated!
Kind regards,
Shimmy
The Demo part was just awesome ... Thanks a lot for the effort
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THANK YOU SO MUCH,
YOUR EXPLANATION IS EASY TO FOLLOW AND UNDERSTAND.
Glad it was helpful!
@ 13:07 min in the slide of calculation P(B|A) = P(WeekDay | Buy) is wrong. It should be 9/24 not 2/6. 2/6 is P(weekDay | No Buy). Is my understanding correct?
I agree with you. The answer for P(No Buy | Weekday) = 2/11, P(Buy | Weekday) = 9/11. These can be verified from the frequency table directly.
Thanks for sharing your thoughts. Cheers!
@19:20 why is the sum of probabilities higher than 1, how does that come about? I thought probabilities sum to 1
16:27 Result is: ≈0.02
17:16 Result is: ≈0.98
19:15 Then sum of probabilities is: ≈1 (AND NOT 1.164, because sum of probabilities of two related events equals 1)
Likelihood of Purchase: 0.98 / 1 = 98%
Likelihood of NO Purchase: 0.02 / 1 = 2%
And then as 98% > 2% we can conclude that customer will buy on a holiday with discount and free delivery.
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I must say your explanation is excellent compared to others. You’re to the point and not unnecessarily stretching, as I’ve seen on other tutorials.
Also Please help me with the data set
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3:11 P(b) = Probability Of event b .?
Thank you for pointing out, Sandeep. P(B) is equal to the Probability of occurrence of event B.
@15:00, I guess it's typo "P(B)=Probability of event A".
Hi, my email is kartha@vivaldi.net
Could you please send me the dataset?
At the loc 13:20 , P(B|A) is nothing but P(Buy|Weekday) and should be 9/24 instead of 2/6.Isn't it?
very clear explanation with great examples, thank you!
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Heyy thank you so much.
I am doing project called order Prediction
my dataset has following columns
count year month day hour working_day weekend_day public_holiday
then how should I apply naive bayes on it?
YOUR tutorials are awesome!!!.Thankx for sharing.God bless u!!
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Hello @ Simplilearn! I have become glued to your videos, please can you kindly help with the dataset too to this email? xxxxxxxxx - It will be greatly appreciated. Thanks in anticipation.
Hi Jay, we are super happy that you love your videos. We are glad to have helped. We have also shared the dataset to your mail ID. Do subscribe, like and share to stay connected with us. Cheers :)
Thanks very much. I subscribed already. And I have the dataset too.
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1. For the first problem (12:00 into the lesson), P(No Buy | Weekday) can also be calculated by seeing that there are 11 visits on the weekday and 2 of them are No Buy. Therefore the answer is 2/11. Which does match the answer via Bayes (if the Bayes calculation does not round the fractions)
2. On the example using Holiday, Discount = Yes and Delivery = Yes, I was surprised that P(No Buy | B) + P(Buy | B) does not equal 1. Given that these are the same condition isn't the probability of Buy or No Buy 100% ?
3. P(A|B) When you doing the classification of articles ... is it true that the likelyhood tables are - The article types as the row headers and the key words as the column headers ... so there would be one very large likelyhood table.
Great insights and fantastic questions.
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TQVM for the Machine Learning series!!!
Our pleasure!
The way you explain is amazing. Could you do a favour by providing the dataset.
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Hello. First of all thanks for the video. Video contains at least two mistakes. One is typo. Other is a logical error which is at 17:09. P(A|B,C,D) (where A ∈ {True,False}) is the question. If P(A=True|B,C,D) = p, then P(A=False|B,C,D) = 1-p because sample space of A contains only "True" or "False". Am I wrong? It is probably calculation error. The typo is at 13:10 (P(B|A)=2/6=0.375), it should be 9/24 instead of 2/6. It is left from the previous slide. That's all, have a good day!
Thanks for your response! You are correct, we are sorry for the error!
Very good explanation. I have a question at 19:36 sum of probabilities is 1.164, how sum of probabilities is greater than 1?
You must normalize the probabilities by dividing both by 1.164, then you have the sum of the probabilities equal to 1. Sorry for my bad english
Why do I see sometimes people ignore the the P(B) in the denominator because it is assumed that it does not change the outcome.
Your videos are amazing. Thanks! I have subscribed!
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thanks for this tutorial. Please i need the shopping cart dataset
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at 19:47 timestamp, it is mentioned as sum of probabilities = 0.986+0.178 = 1.164. but, probability can range between (0..1). Can you please explain how sum of probabilities can exceed more than 1?
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Great tutorial again SimplyLearn you guys are amazing for simplifying the topics! can you please share the dataset for this!
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Great explanation, ty
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Fantastic explaination....can i get dataset for above classification test?
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Great content that you so much!
Glad you enjoy it!
Just what I was looking for thank you so much.
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Excellent 🌙
Thank you! Cheers!
was really helpful to understand!! Can you please share this dataset for practice?
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Hello thank you for this interesting explanation.
please how to i get the dataset you used for this?
thank you
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
Thank you Simplilearn team for the clear explanation. Can you please provide the dataset and the python notebook used in the video?
Hi Aisha, we are glad you found our tutorial informative. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
why are we transposing the matrix in the case of confusion matrix in @37.02
This is superb.
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very helpful
Glad to hear that
Good video on Naive Bayes classifier
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This is on a slight tangent, but i gotta ask: why do you call numpy (py as in python) "numPEE" and sci (sci as in science) as "SKY"?
Nice and useful! Just curious to ask a question. As far as I understand, in the shopping use case, we used three features (Day, free delivery & discount). And, in the text categorization use case, we used one feature (text containing email content). Is that really treated as one feature here? Or, will the naive bayes algorithm, internally creates multiple features for the given input (text containing email)?
Our feature in the use case of text categorization is "each individual word". Here, we train the model to predict the probability of this particular work being in any of the different categories. The final probability is derived by calculating the overall conditional probability of that particular combination of words (just like we found the probability of different combinations of day, free delivery and discount).
Great Tutorial! What I'm searching for!
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I don't know if it's just me but i think it would've really improved the tutorial if you at least had a mouse pointer pointing where you are explaining.
Or the text clould've appeared progressively.
I think it makes it hard to follow the tutorial. My eyes just don't know where to look at.
Thank you for letting us know about this. Your feedback helps us get better. We are looking into this issue and hope to resolve it promptly and accurately.
Wonderful tutorial! Great work and thanks! :)
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2:38 there is a typo P(B) should be probability of event B and not A
That's correct. You have an eye for detail. Cheers!
@19:20 why is the sum of probabilities higher than 1, how does that come about? I thought probabilities sum to 1
Just subscribe to your channel, you do a great job explainig I love it!
Thanks for subscribing to our channel. We welcome you!
@@SimplilearnOfficial I'm just having a little bit of problem with the dataset, do you have a video where you show how to do them and how you assign the right tags to the training group?
Hello, I really found the conceptual part easily comprehendible. Can you please send me the dataset?
Hello Shubhangi, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
How can we check for the accuracy on test set for the above 20 news group model????
Thank you for the presentation. Love to see the data set.
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Thank you for taking your time to prepare this, it's very helpful. I'll appreciate if you can share the dataset. Thanks again.
Here's my mail : jmiri@josagro.com.ng
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Why you haven't put data set link in description ? You are collecting email ids of people by asking it to provide datset.
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
This was really helpful to understand. Great tutorials!!! Can you please share this dataset for practice?
Hello Gaurav, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
@@SimplilearnOfficial plz share dataset on chandrasejal06@gmail.com
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Hi! Great tutorial! Could you please send me the dataset?
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
Thanks for the video!! Can you share the dataset plz?
Hello Trisit, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Absolutely loved it!! Can you please forward me the data sets?
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Great tutorial. Please can you send the datasets please. Thanks in advance
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
You took mat variable for confusion matrix, can you tell me what is the difference between mat and mat.T??? (mat and mat.T both are printing different tables)
mat.T is the transpose
Thanks a lot!
Great class, please send me the data set.
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
thank you ^^
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why is P(A|B) plus P(NOT A | B) not equal to 1?
16:34 Why P(Discount = Yes, FreeDelivery = Yes, Day = Holiday | No) = P (Discount = Yes | No) * P (FreeDelivery = Yes | No ) * P (Day = Holiday | No) ? Are P (Discount = Yes | No), P (FreeDelivery = Yes | No ), P (Day = Holiday | No) probabilities of independent events?
great content. can you provide me the dataset ?
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we can send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that too. Hope that helps.
Hi Simplilearn, It was really a nice tutorial and the example of Shopping was superbly explained. I have one doubt regarding code, when you were providing some different input like "Presedent of India", then you were getting output(lable) correctly. How was that working?
This works on the principle of text categorization. We have used Naive Bayes to achieve this in this video, though other algorithms could also be used for the same purpose.
Keep up the good work Simplilearn team, please send me the dataset mentioned at around 8:41
Hello Lampa, thanks for viewing our tutorial and we hope it is helpful. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly.
Thanks very much for the swift and positive response, here it is >> lampapoultaal@gmail.com
Awesome tutorial..... :) Dataset please???
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@@SimplilearnOfficial do you want to share my ID here...?
You can post it in the comment section and we will send you required file directly. If you want your email ID to be kept hidden from others, we can do that as well. Hope that helps!
@@SimplilearnOfficial can you send me the dataset also
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@13.24 - P(Weekday|Buy) should be 9/24. In slides it is 2/6
Also could you please send the data sets @ manishgaurav84@gmail.com
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great tutorial, very easy to understand
Please can i have a dataset to test your demo
Hello Maxwell, thanks for watching our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
I have twitter text data around in millions , that i fetched from twitter based on the terms ' Coronavirus'. 'COVID-19', Now i put some labels manually to approximately 2000 posts, Now can i use this algorithm to classify the remaining textual data based on my labels that i gave? PS: I classified the data into eight textual categories! And also for text data classification which algorithm is best ?
Awesome explanation, could you please share me the dataset. Thanks!!
Hello, thanks for appreciating our work. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Nice tutorial. Pl confirm how to import classified text in CVS or excel file.
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@@SimplilearnOfficial sheevani.thakur@gmail.com
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@@SimplilearnOfficial I am unable to open a middle file you have sent. Pl confirm, have you created a data set in excel.
@@SimplilearnOfficial I have sent you an email pl check.
13:28 P(B|A) is 9/24 (0.375) not 2/6 (0.333...), but the result is correct.
Thank you so much for bringing this to our attention. We reported this right away to the relevant department.
wow thank you so much!!! excelente content !!!
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Great tutorial, could you please share the dataset?
Hello Neema, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Wonderful Video! very helpful. Can I please have the data set. Thank you
Hey Dean, thank you for appreciating our work. We are glad to have helped. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. Cheers!
Hello thank you so much for this!. If possible, can I get the shopping data to try and analyze myself in Python? Thank you!
Hello Nyjah, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Amazing explanation, please could you send me a copy of the data set - many thanks for these tutorials!
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Hi could please share the database used for the demo? Thank you
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There is an error at 13:09 when P(weekday| buy) = 2/6 however it should be 9/24.
Good eye! Our mistake.
awesome explanation..could u please share the code and dataset..it will be really helpful
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@@SimplilearnOfficial cricketfanhere@gmail.com
Hi thank you for this great video.
could you share the dataset please
Hello Chantal, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
At 12:56 you say 2/6 equals 0.33, at 13:12 you say 2/6 equals 0.375. P(Weekday | No buy) is truly 2/6, but P(Weekday | Buy) is the opposite, 9/24.
At 11:05 what? You WRITE P(Weekday), but SAY the probability of making a purchase on a weekend, which would be P(Buy | Weekend). Then you say that the P(Buy | Weekend) is 11/30, but it is 7/24.
Nice one! i have almost downloded all the videos here pls, help me with the datasets for the tutorial so as to practice it plss
Hello, thanks for viewing our tutorial. It would be helpful if you will provide your email ID to us so that we could send the requested dataset promptly. On the off chance that you need your email ID to be kept hidden from others, we can do that also. Hope that helps.
Hi
It's a very good tutorial. Would appreciate if you can send the dataset.
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Nice Video sir can you please share this dataset.
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Hello! Thanks for creating such a wonderful tutorial! Could you please send me the dataset?
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@@SimplilearnOfficial Hi! Thanks for reply. My email is careywang1231@gmail.com.
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At 3:10 mins, P(B) has been mentioned as probability of (A) , this should be probability of (B) .
Good eye! It's our mistake.
Thanks for the video. I couldn't catch (for the purchase&no purchase example) why the sum of probability of a purchase and probability of no purchase doesn't make 1. When B = (Day=Holiday , Discount=Yes , Free Delivery = Yes) , there are only 2 possible results , buy or no buy. So the sum of the probabilities should equal 1 , isn't it?
I too have the same doubt, I think the clarification given earlier is ambiguous.
We need to normalize the conditional probabilities and these normalized probabilities do add up to 1 (Go to 19:14 in the video).
We need to normalize the conditional probabilities and these normalized probabilities do add up to 1 (Go to 19:14 in the video).
@@SimplilearnOfficial
There is a calculation mistake @ 15:35 .
I think your intuition is wrong, however, I'm not sure as I am still very inexperienced in the field(student).
However there is a normalization probability in Bayes(denominator).
Please provide dataset for all the algorithms
Hello, thanks for viewing our tutorial. You can find your requested dataset in the video description. Hope that helps.
Dataset please ?? Will be helpful,
thanks in advance
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@@SimplilearnOfficial faizii.jay@gmail.com
Hi Jay, we are super happy that you love your videos. We are glad to have helped. We have also shared the dataset to your mail ID. Do subscribe, like and share to stay connected with us. Cheers :)
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Probability Probability Probability Probability and done
Great tutorial video! very well explained. Could i have the dataset so i could use it.
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video is very interesting and useful. I would like to work on the dataset can u please share
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@@SimplilearnOfficial Sir, I haven't received any email regarding dataset yet!
Thanks for uploading such type of videos. kindly send me the dataset thanks
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Thanks for such a great tutorial! Can I have the dataset, please?
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@@SimplilearnOfficial can you send me the Dataset? My Email is thies.schomaker@gmx.de
great tutorial, can you please send the dataset? thanks.
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Please can you give dataset of shoping demo
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thanks for the video, can I have the dataset?
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nice video. thank you , can you please help mi with the data set
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good video .can I get the data set please?
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great tutorial. Can you please send me data set?
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@@SimplilearnOfficial thanks you. saurabh.cegian@gmail.com
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Great tutorial! Send me please the dataset.
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Hi thank you for this video! Please could i ask for the dataset and source code?
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Nice video , it would be really helpfull if i had dataset can u please send it
my mail id is nikhilbadgujarand@gmail.com
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