Thanks for creating DM playlist and explaining evth. so simply and in an easy way. It is really helpful. Thanks again for creating wonderful videos. Stay safe. Tc.
lots off efforts mam thank you for this❤ knowledge our exams are near no need to read aal the stuff just watch your videos 2 times and done for this topic for exam😊
Outstanding video. Thoroughly and articulately summarizes each aspect of data preprocessing. Will definitely return to this channel in the future for more help!
it's 3 am right now and I'm preparing for my exam which is at 9 am and this is very very helpful at the last moment, couldn't thank enough for this ma'am 🥺❤️
Mam... ur voice is so sweet... Don't misunderstand me... Really ur voice is so sweet.... I jst loved it.... If u feel bad.. Iam extremely sorry mam... Thank u
08- Data Preprocessing In Data Mining (the process of transforming raw data into an understandable format) Four major tasks ------------------------------ 1- data cleaning - removing noisy data (incorrect, incomplete, inconsistent data) and replace missing values For missing values, replace with N/A, a mean value (normal), a median value (non normal) or most probable value manually for small data sets, automatic for large data sets -1 Noisy data -> binning, sort data, assign into bins smoothing process - remove error values -smooth by bin mean -smooth by bin median -smooth by bin boundary (min or max values) -2 Regression - numerical prediction of data -3 Clustering - similar data items are grouped at one place dissimilar items - are outside the cluster 2- data integration - multiple heterogeneous sources of data are combined into a single dataset Two types of data integration 1- tight coupling - data is combined together into a physical location 2- loose coupling - only an interface is created and data is combined and accessed through the interface data is stored in the DB 3- data reduction - the volume of data is reduced to make analysis easier methods for data reduction 1- dimensionality reduction reduced the number of input variables in the dataset, because large input vars -> poor performance 2- data cube aggregation - data is combined to form a data cube and redundant noisy data is removed 3- attribute subset selection (attributes are columns) highly relevant attributes should be used, others are discarded (data is reduced) 4- numerosity reduction - store only a model (a sample) of data rather than the entire dataset 4- data transformation - transformed into appropriate form suitable for the DM process Four methods ------------------------ 1- Nominalization - scale the data values in a specified range (eg; -1.0 to 1.0 or 0 to 1) 2- Attribute selection - new attributes are created using older ones 3- Discretization - raw values are replaced by interval levels eg; 10,12,13,14,21,22,34,36 -> 10-20, 20-30, 30-40 4- concept of hierarchy generation - converting attributes from a low level attribute to a higher level attribute eg; city -> country .
Super mam ..it's very helpful those who r studying one night before exam ❤️
😂😂😂
bro bro behave like a matured one bro..... one night studying or seeing videos here????????
😂😂
@vela thy mother
Now I'm doing it bro😅😅😅😂😂😂
Legends watched it till the end. Thanks ma'am it's easier than other tutors.
The best thing in your videos is every topic is broken down into simple one. We are waiting for more and more subjects.
Thanks for creating DM playlist and explaining evth. so simply and in an easy way.
It is really helpful. Thanks again for creating wonderful videos.
Stay safe. Tc.
lots off efforts mam thank you for this❤ knowledge our exams are near no need to read aal the stuff just watch your videos 2 times and done for this topic for exam😊
All the steps of pre-processing are explained very good and that too in simple language
Outstanding video. Thoroughly and articulately summarizes each aspect of data preprocessing. Will definitely return to this channel in the future for more help!
Awesome, thank you!
Really well explained , it's a perfect combination of detailed but brief , all clear thnks to you❣
Really Thanks Mam for easy content to understand and ur way of teaching is really awesome ⚡✨.
Thank you so much best content on internet with explanation thats what we need😌
Ma'am can you upload unit 2 , because we have exams on 11th , and thanks for videos we love ur videos ❤️. We are waiting for further videos
My whole topic in 18 minutes! Great Job!
Ma'am your teaching style is too good ,easily understandable.would you please upload SUPPORT VERTEX MACHINE(SVM)in data mining...❤️
Fuff!! OMG your voice is so calm and clean. ♥
Mam my exam tomorrow and this was a gamechanger for me thank you so much🎉🎉🎉🎉🎉🎉🎉
Best explanation according to computer science and business systems data mining and analytics syllabus.
this video helped me so much with my exam thank you
Thanks alot mam actually I enjoyed the video and well understood ❤
superb sister, i understand the whole section, thank you for taught the topic to make easier. one again thank you😍
You are welcome 😊
Tq for explaining the preprocessing mam ......it will help me for my exams
Thank you so much madam ❤❤❤
Thank you mam your content and information is very useful in writing exams
Ah don't worry about the duration... We watch in 2x anyway. Keep up the good content.
❤❤❤❤ Thank you so much Mataji
it's 3 am right now and I'm preparing for my exam which is at 9 am and this is very very helpful at the last moment, couldn't thank enough for this ma'am 🥺❤️
Thank u mam , it's very helpful for xams😊😊
Tq... For simple and good explanation
Mam...
ur voice is so sweet...
Don't misunderstand me...
Really ur voice is so sweet....
I jst loved it....
If u feel bad.. Iam extremely sorry mam...
Thank u
Really great teaching and great efforts
Perfect... precise....to the point 💯💯❤️
A very thank you madam ji 😊
super explanation mam
Super mam,it is very useful for exam preparation..
Thank you
No words to say........🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏
Mazza aa gya, Thank you ma'am !!
Nice, Explanation mam👏
Thank you for your valuable information ✨✨
Great effort mam❤❤❤
Awesome,just loved it
So good🥺🥺🥺.....thank you!! ❤️
Very clear explanation thank u mam
Really so useful mam 😊❤️
Good explanation mam😍
Thankyou.
Good job, it would great if you can include diagrams also.
Nice explanation 🎉
It was very helpful
Really thank you.
It's really useful.
Please upload videos about association rule mining and classification.
greate explanation
A good session madam
thank you madam
08- Data Preprocessing In Data Mining
(the process of transforming raw data into an understandable format)
Four major tasks
------------------------------
1- data cleaning - removing noisy data (incorrect, incomplete, inconsistent data) and replace missing values
For missing values, replace with N/A, a mean value (normal), a median value (non normal) or most probable value
manually for small data sets, automatic for large data sets
-1 Noisy data -> binning, sort data, assign into bins
smoothing process - remove error values
-smooth by bin mean
-smooth by bin median
-smooth by bin boundary (min or max values)
-2 Regression - numerical prediction of data
-3 Clustering - similar data items are grouped at one place
dissimilar items - are outside the cluster
2- data integration - multiple heterogeneous sources of data are combined into a single dataset
Two types of data integration
1- tight coupling - data is combined together into a physical location
2- loose coupling - only an interface is created and data is combined and accessed through the interface
data is stored in the DB
3- data reduction - the volume of data is reduced to make analysis easier
methods for data reduction
1- dimensionality reduction
reduced the number of input variables in the dataset, because large input vars -> poor performance
2- data cube aggregation - data is combined to form a data cube and redundant noisy data is removed
3- attribute subset selection (attributes are columns)
highly relevant attributes should be used, others are discarded (data is reduced)
4- numerosity reduction - store only a model (a sample) of data rather than the entire dataset
4- data transformation - transformed into appropriate form suitable for the DM process
Four methods
------------------------
1- Nominalization - scale the data values in a specified range (eg; -1.0 to 1.0 or 0 to 1)
2- Attribute selection - new attributes are created using older ones
3- Discretization - raw values are replaced by interval levels
eg; 10,12,13,14,21,22,34,36 -> 10-20, 20-30, 30-40
4- concept of hierarchy generation - converting attributes from a low level attribute to a higher level attribute
eg; city -> country
.
Look! How cute she sounds 🥰
Nice teaching skill great 👍🏻
Thank u so much 😊😊
Sampling and regression in data mining plzz keep ur video on that topic
Thank you so much mam it is really helpful
thank you for the lecture
Nice lec maam thank you
It's very useful for me
Maje a content on english spoking skills mam, your spoking skills are really too good and it is useful for us in interviews
Tq you so much madam❤️
Nice explanation mam
Best Indian- English accent ever
Thank you mam❤
Well explained
Explain index sequential acces method (ISAM) plz😊😊😊😊😊
Please share that written notes
Thanks ma'am ❤
Very understandable
Thankyou..❤️
Got it😃
great mem
Voice ♥️♥️
Love you mam❤
Make videos on software process and project management we have exam on Feb 16
Plz explain cart concept on your next video
easy to understand thankyou
Thank you very much.
Thank you so much mam...
Very helpful
what of the code for the data preprocesing
11:38 u said there are 7 methods but in video there Are only 4 .. why so?
make videos on FUNDAMENTALS OF BIOMEDICAL APPLICATIONS
🎉🎉
video opening noise>>>>> her low voice
akka nen fan akka ni voice ki
Mam please give notes
thanks!
Great
Mam can you upload data ware house architecture and implementation
Can i have a video about satges in data mining and KDD vs Data mining
plzz
Thank you so much for this video 🥹❤️
long video but worth it video
Best , got my
didi thanks a lot
❤❤
can i get the soft copy of the notes ??
Plz provide the link for your handwritten notes mam
Plz...mam upload more vedios .we have exam on Feb11th...
THANK YOU MAM...
Thank you mam