Apriori Algorithm in Data Mining And Analytics Explained With Example in Hindi

[@kevinboghani272]

Spent 40 mins on its theory and couldn't learn shit. 5 mins into this video and I feel like a pro. Thanks bro!

[@prashantmhatre9225]

I have done my engineeraing in 2010, But agar aap tabhi rahte to bhai , mai topper ban jata tha... awesome explanation , thanks

[@ashishdass1605]

You are a saviour bro....6 years down the lane and this video is helping students pass their semester exams... you have our good wishes with you bro❤❤

[@gautamverma9762]

Possibly the best explanation of apriori algo out there on YT.

[@Jitendrachouhan1999]

Sir if you are reading my comment .. In these questions have 1,2,5 you forget but our answer is Accurate. Thanks for making that kind of video 👍

[@animelover5271]

yes bro 1,2,5 is also triplet

[@hemrajbudhathoki9436]

@@animelover5271 it is triplet. However, 1, 2 is rejected in step 2. so the set which contains 1, 2 is rejected.

[@divyamarora3071]

@@hemrajbudhathoki9436 then what about {1,2,3}?

[@samyakjain3239]

@@divyamarora3071 Its {1,3,2}

[@Hackve-l7z]

Bhai abb kyy krta hai tu placement mili kyy

[@Gfghbf]

sir u are supremely talented i always watch ur videos they are so useful !!!

[@dude-ox2em]

Ivideyum Malayali💪🏻

[@VoyagerVlogs]

Aaj ka video bada kamal ka hua !!!! Thanks

[@09khushalbheke42]

Sir this videos are very useful for us because our college teachers don't teach like you, I request you please keep doing videos for us

[@ShivaniOnYT]

6:06 1,2,5 ka bhi 1 hai support

[@sajidrsheikh]

He must have forgotten about it. 1,2,5 support is 25%

[@ankitmukherjee7036]

But (1,2,5) ka second step me elimination hota hai hai jab (1,2) and (1,5) k probabilities 1/4 rehta

[@anujmore7547]

@@ankitmukherjee7036 Yes u r right

[@rahulchaubey8988]

yes correct

[@Kashmixx]

Exactly❤️

[@cybershrajal]

sir kal exam hai ML ka apriori rat ke ja raha hu aapke chanal se .bhagwan kare aa jaye bas 🙂🙏🤍

[@abhyudaypratap366]

Are sir ji maja aa gaya keep it up I like the way u are teaching Salute h sir aapko

[@neatelf9913]

When you get a girlfriend for the first time 4:19

[@jhamukesh998]

double penetration on 5:38

[@faizanullahkhan1282]

@@jhamukesh998 I'm crying here 😂😂😂

[@user-rr2hz3ti6l]

🤣🤣🤣😅

[@emrem794]

Dude u should do an English Version. Even I don' t know Hindi, I understood the basics of this concept. Thank you

[@sunrise7964]

Such a fabulous faculty I have never seen in my life

[@rucha520]

Good video👍🏻 Also Acc. To anti monotonicity if item set violates contraints so will its superset. So only {2,3,5} should be considered for last table.

[@noneofurbusiness565]

so anyone who does not understand this comment : she means to say that for {1,2,3} there is 3 subset {1,2} , {1,3} and {2,3} within which {1,2} does not have min support >=50%(check previous step) so the super set {1,2,3} is invalid same is for{1,2,5} and for {1,3,5} in which {1,5} does not have min support >= 50% . there was a lot of confusion in comment section so i hope this helps

[@Abhishek-kr8ci]

@@noneofurbusiness565 yes it does thanks

[@Pranav-vn8bp]

Bhai mera ek suggestion hai ki tu agar "english ke subtitles dalega to tere views aur subscribers dono badhenge"... I know it'll be a time consuming task but Mark my words this will really help "you grow in life" n international viewers will love it. I'm from India and as an Indian I feel you'll make us proud by doing this.

[@samriddhisingh1649]

Thankyou sirrr❤ 2 phone se subscribe bhi krdiye 😩❤️

[@shreechatane9215]

So well explained I can write a cpp code for this algorithm 🔥

[@montusgamer1234]

thank u sir kal mera exam tha aur apki video dekh kar samaj agya. Btw is there any chance agar ap online classes de sako?

[@siddugoud1294]

I like your smile in the beginning...

[@funnysid8180]

Tum to dhamaka krdiye bhai ❤️❤️❤️

[@fahimchowdhury6742]

YOU SAVED ME BROTHER 💚

[@rajendramankar8226]

छान!!👌👌👌👌

[@vanisrikumar8741]

U saved 10 mins of my tum TQ❤️‍🔥

[@tejasamaresh9900]

SIr, I am not able to find your video on Association rules which includes the topics dedicatedly on Support confidence and Lift ratio. Could you pls attach the link.

[@gourav1163]

Saari video chaant maari kisi ne bhi confidence and support nhi btaya ki ye actually me hota kya hai😑

[@paper-toon1012]

Chhod de Bhai.....

[@GangaramSahu-xj6hd]

Yeah that's true bro Koi explain nahi karta theekse,.. I'm trying here maybe you get this.. 1. Support: Simply the percentage of occurrence.. (occurence of single item or 2 items together etc) in given dataset.. 2. Confidence: it is conditional probability, like given a what is the probability of b.. Let's see one example, if we have a database of people buying shoes 👟 and socks 🧦, so total number of times they both purchased together divided by total number of transactions is SUPPORT, and If I buy shoes then I want to know like how many people buy socks after buying shoes then that's CONFIDENCE, SUPPORT = 👟+🧦 / no. transactions CONFIDENCE = 👟+🧦 / 👟

[@kripadhrangdhariya9194]

Support: Think of it as popularity: It tells you how often two items are purchased together. Example: Let's say out of 100 customers, 30 buy both lemonade and cookies. So, the support for "lemonade with cookies" is 30%. Confidence: Think of it as reliability: It tells you how likely someone who buys one item (let's say lemonade) will also buy the other item (cookies). Example: Out of those 30 who bought lemonade, 25 also bought cookies. So, the confidence for the rule "if someone buys lemonade, they also buy cookies" is 25/30 (around 83%). Finding Rules: Gather data: Track customer purchases (lemonade, cookies, etc.). Calculate support: See how often combinations appear (e.g., lemonade & cookies). Calculate confidence: For each combination, see how often the second item is bought with the first (e.g., how many who bought lemonade also bought cookies). Set thresholds: You decide what level of support and confidence is interesting. For example, you might only consider rules with support above 20% and confidence above 70%. Example Rule: Rule: "If someone buys lemonade (antecedent), then they are also likely to buy cookies (consequent)." Support: 30% (30 out of 100 customers). Confidence: 83% (25 out of 30 lemonade buyers also bought cookies). This suggests that since many people buying lemonade also buy cookies, having cookies available might increase your sales. Remember: High support means the combination is popular, but it doesn't guarantee one causes the other (people might just like both). High confidence strengthens the connection between the items. One of the most common algorithms used for finding frequent itemsets is called Apriori. Here's a breakdown of how it works in simple terms: Imagine you have a basket full of groceries: Each item in the basket represents an item in your data (e.g., milk, bread, eggs). You want to find out which combinations of groceries appear together frequently. Apriori works like this: Start small: It begins by looking at individual items and counting how often each appears in your data (like counting how many times you see milk, bread, and eggs). Find frequent single items: Based on a minimum support threshold (like a minimum number of times an item needs to appear to be considered frequent), it removes any item that doesn't show up often enough. Level up: Now, Apriori focuses on pairs of items. It combines the frequent single items from step 2 to see which pairs appear together often. Again, it eliminates any pairs that don't meet the minimum support. Iterate: This process continues. Apriori takes the frequent pairs and combines them to form triplets (like milk, bread, and eggs), checks their frequency, and removes any that don't meet the threshold. This keeps going as long as it can find frequent itemsets at each level. Key points about Apriori: Candidate generation: At each level, Apriori creates a list of potential frequent itemsets (candidates) based on the frequent itemsets from the previous level. This is why it's called a candidate generation approach. Iterative and level-wise: It works its way up one level at a time, finding frequent itemsets of increasing size in each iteration. Pruning: To avoid checking unnecessary combinations, Apriori uses a clever trick. It knows that if a smaller subset of items (like milk and bread) isn't frequent, then any larger set containing those items (like milk, bread, and eggs) cannot be frequent either. This helps reduce the number of candidates to evaluate. Think of it like building a pyramid: You start with the single items as the base (frequent single items). Only frequent pairs can be placed on top, forming the next level. As you go up, only combinations based on already frequent items are considered, ensuring all levels are built from frequent building blocks. Apriori is an efficient algorithm for finding frequent itemsets, but it can be computationally expensive for very large datasets due to the repeated candidate generation and support counting. There are other algorithms like FP-Growth that address this issue.

[@gourav1163]

haha thanks but I think it's a little too late for me😅....well it will help others.@@kripadhrangdhariya9194

[@semdantani1]

Support bhai wo hota hai jis se combination ki popularity bata ta hai mtlb konsa combination sbke jyada frequently use hota hai or confidence hame bata ta hai ki koi particular a ko lege to b ko lene kitan jaruri hai jese ki milk ke sth bread

[@Sheetalmanohar]

Really good explanation. Great job

[@anshulthakur3806]

Awesome Tutorial

[@milansanadhya9708]

Very nicely explained

[@SelieJaramillo-v2w]

The video is very interesting! Something I don't understand: I have USDT in my OKX wallet and I have the recovery phrase. -----------: How should I convert them into Bitcoin?

[@shru1244]

Apriori algorithm is used to find Association between the two objects. End goal / objective of apriori algorithm is to get the association rule between the two objects.

[@akshayhandgar9492]

Congrats for 10k views. Hope adsens are add on next time🔥🔥👏

[@akshatsharma5774]

Sir isme 3rd step me {1, 2, 5} ka pair nhi bnega?

[@Bollywood_beatz]

Correct i was also thinking the same

[@RishabhJain-ny1ze]

Nhi Jo pair hmnare pas second m aaye h surf unse hi bnate h

[@nuctanKatira]

@@RishabhJain-ny1ze hey brother! i want to know ,, how to make pair in this step mean ..., from (1,3)(2,3)(2,5 )(3,5) this set ... how to make ?

[@RishabhJain-ny1ze]

@@nuctanKatira bro just take those pairs where something is common and write those pairs and write the common element once

[@mrinalsharma18]

rather than finding support percentage of each dataset you could have change the minimum support percentage to numeric ,it could be way more easy to solve and to understand.

[@AB-we2vm]

7:00 sir what about 2,1,5 ? Plz ans

[@anubhibudakoti6247]

{2,1,5} is ignored, as he already told....bcz it was having the support of 25%

[@leovaldez9498]

@@anubhibudakoti6247 but so does {1,3,5} and {1,2,3}. he shouldve mentioned it

[@abhijeethirekhan5175]

Aaj ka ye video kamal ka hai

[@godofwar8262]

Items set ❌ Atom set ✅

[@sudhanshuranshevare]

Thank you so much sir❤❤

[@RanjitSingh-rq1qx]

Sir your content is mind-blowing but less subscriber . I don't like it. But sir u don't very sir, i will promote your content with my friends 🥰🥰❤️

[@dharmendra.pandit]

Hello and welcome, dosto, to Five Minute Engineering. Aaj ka video vakemein hi kamal ka hone vala hai.

[@sadaffatima853]

Thank you so much . great explaination

[@digvijay_karande]

Kadddak 🤜🤛

[@BritBlissAdventures]

Very nic super explanation

[@deepakalinavlogs]

Good wishes. Loved it.

[@m00ndr0p]

Thank you. Great tutorial !

[@pragyajais4645]

Thank you Sir... You are too good , this video is very helpful for me

[@mathematicians5925]

Very nice Explanation Sir 😊👍👍

[@humayunnasir6261]

good explanation bro . Love from Pakistan

[@tulsikhatri8694]

Great Great Great explanation...

[@dhairyamorbiya467]

clear and precise🙌🙌

[@vikatagamingyt3251]

Awesome video sir🥰

[@jashanbhayana8089]

there should be {1,2,5} also in the last table

[@vijayjangir3556]

The itemset III that you've generated, shouldn't include {1,3,5} and {1,2,3}. the only valid candidate after candidate pruning as per F(k-1) would be 1 itemset. that is {2,3,5} This is as per the anti-monotonic property of support. and the foundation fo apriori as opposed to bruteforce.

[@cricketvidz6808]

Agreed

[@mrsmile1387]

heyy could you pls tell the details of the above theory pls

[@sultanurrahmanshuvo]

very helpful video sir

[@umar-tariq]

Item ❌ Atom ✅

[@sanketpawar9417]

[@chandankhuntia7017]

beautifully explained

[@tapanjeetroy8266]

Thanks a lotbsir.. You have done a great job

[@anmol_28]

Triplet should also include {1,2,5}

[@priyankitanwar3491]

because {1,2} and {1,5} are discarded so there's no common ground to group them

[@techeasy602]

{1,2,5} occurs only one time which gives 25% support that's why it is discarded

[@hardiksingh8119]

Gyaan mat pel zyada bhadwe

[@Akuma7499]

Yes

[@charugera7654]

Excellent explanation

[@stephyjohn4517]

Sir, I kindly request you to share video on graph mining( apriori based approach for mining frequent subgraphs).Your videos are helpful and easy to understand.Hope to see more videos from you.

[@switugohil03]

Super work...go ahead

[@satyajitdas3120]

Sir minimum support and threshold confidence agar qsn main na deya ho tohh ??? Kya pakar na hai ?🙄

[@harshpandey8245]

Hi, in the 3rd step of triplets, couldn't we add (1,2,5) too- wo bhi to ek triplet hai? that also has a support of at least 1, i.e. (1,2,5)=1/4=25% Please confirm!

[@priyankitanwar3491]

because {1,2} and {1,5} are discarded so there's no common ground to group them

[@adarshtapariya4275]

@@priyankitanwar3491 but (2,5) is there

[@vikramadityasingh4979]

Sir why didn't you include (1,2,5) in triplet formation? That triplet is present in TID 300.

[@priyankitanwar3491]

because {1,2} and {1,5} are discarded so there's no common ground to group them

[@abhimanyouknow]

@@priyankitanwar3491 then how did we get {1,2,3}?

[@sougatasen3691]

Yes he should have included {1,2,5} But it would have been left out eventually cause it's support would have been 1/4=25%

[@Lionelmessi-zp9vt]

@@sougatasen3691 yeah this might be the reason

[@rdxgaurav3483]

@@abhimanyouknow cause {1,3} is still in the pair set

[@amarsharma466]

Nice, easily explained

[@DipanGhosh_Dipdu]

Great explanation

[@yamzaidi]

5:50 we can have (2,3,5) is also a possible triplet then why you didn't include it in III matrix ????

[@piyushapawar4640]

(1,2,5)*

[@mukeshjha4280]

Hit like if this guy saved you today.

[@jaykothari6818]

Awesome explanation....

[@machinelearning6846]

Please make brief about gradient descent algorithms.

[@shreyashachoudhary480]

Superb!

[@tulsikhatri8694]

Engineering exams, exams time pe pad ke nikal rahe hai tumhare videos se...

[@anikacharjee3932]

Wow!!!!!! Just Amazing

[@MalikQasim-ri4oq]

Really appreciate

[@kamaljeetkaur8653]

Thnk uuu sirrrr😊😊😊😊😊😊😊😊😊😊😊😊😊😊

[@MdRashid-zx6cn]

Thank you sir for video

[@bisheshtalukdar2163]

what if in 3rd itteration all item set cant meet min support

[@ankitsuman8205]

Thank you ❤️

[@moeenkhan1066]

Thanks for this video 😊

[@renewed_radiance]

sir how to determine min. the threshold? can we choose any value we want ? Or is it pre defined ??

[@chainsawman-ilia]

given

[@yashraj5766]

sir please upload video on closed frequent itemsets and maximal frequent itemsets....

[@anilkumarpasvan9462]

Min sup. Count 50% ko 100÷50=2 bhi le sakte hai n? So that we have no need to find %,again and again.

[@AbhayTewari-lc3hc]

Sir, in the last table {1,2,5} should also be included

[@teenandrogamer2937]

Pukk ga

[@akashbhosale6353]

Thank You Sir

[@25kirtan]

thanks bro!

[@manshalkhatri9289]

Helpful

[@divyanshsao3846]

What if we have more than 3 elements after the triplet formation , lets say if we have 4 items remaining then what should we do ?

[@siddheshnikam5741]

Does order matters in forming pair?

[@sushil4211]

sir.. mining various kinds of association rules explain Karroo Na.

[@nikitasinha8181]

Thank u so much

[@vidyashreerayar7417]

could 1,2,5 also be a combination? although it would ge eliminated as support doesn't meet threshold.

[@roshantimilsina7354]

yes surely one combination of triplet 1,2,5 is missing

[@sumantrinidas2031]

probably because {1,2} and {1,5} were eliminated in the second step.

[@prashantjannu2652]

Sir your knowledge and teaching is good but it's very irritating voice, Plzz use mic sir

[@lordkrishna7480]

Can u please further videos explain in English

[@anmolagrawal4401]

What was the role of threshold confidence?

[@shalabhbhatiya2292]

sir why you are not taken {1,2,5}

[@himanshunikam6251]

Isma or association rule ma kya diffrance ha?

[@bhavishamoghe2036]

while doing the triplet one why didnt you took {1,2,5} item set???

[@priyankitanwar3491]

because {1,2} and {1,5} are discarded so there's no common ground to group them

[@bhavishyaarya6520]

I also have the same dought

[@badal8435]

Thanq sir

[@gauravyeskar1809]

Sir firse ek vdo banao jisme clear clear smj mein aaye. Iss me kuch palle nahi pdraha

[@akshayjadhav216]

Sir plz Data analytics ke remaining videos dalao naa sir...plz....C4.5, CART, evaluating decision tree, Smoothing, Diagnostics, classification of diagnostic, sir plz hosake to last 3 unit Book mein ke syllabus topic explain kro naa sir bahoot hard language hai sir plz