DMBI | Naive Bayes | Apriori | Confusion Matrix | Binning | Sem 6 IT Mumbai University PYQ Dec 2024
Автор: MATHS WALLAH SURAJ MAHTO
Загружено: 2026-02-22
Просмотров: 38
Описание:
In this video, I have solved Mumbai University Sem 6 Information Technology PYQ (December 2024) step-by-step with detailed explanation.
✅ Topics Covered:
Naive Bayes Classification (Numerical + Categorical)
Apriori Algorithm (Frequent Itemset & Association Rules)
Confusion Matrix (Accuracy, Precision, Recall Calculation)
Data Smoothing & Binning (Equal Frequency, Mean, Boundary)
This video is specially helpful for:
✔️ Sem 6 IT students
✔️ Mumbai University exam preparation
✔️ Data Mining & Business Intelligence (DMBI)
✔️ Last-minute revision before exams
I have explained every concept clearly so that you can understand logic + exam presentation format.
If you are preparing for MU exams, make sure to watch till the end and revise properly.
📌 Like, Share & Subscribe for more PYQ solutions and placement preparation content.
🧠 Questions Covered in This Video (With Explanation & Steps)
⏱️ 00:00 – Introduction
⏱️ 00:57 – Confusion Matrix Concepts
⏱️ 06:15 – Confusion Matrix (Accuracy, Precision & Recall)
⏱️ 10:16 – Apriori Algorithm (Frequent Itemset + Strong Rules)
⏱️ 21:52 – Naïve Bayes Classification
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⏱️ 34:05 – Data Preprocessing (Equal Frequency Binning)
Partition data into equal frequency bins
Smoothing by Bin Means
Smoothing by Bin Boundaries
Fully solved numerical example
🎯 Why This Video is Important?
✔️ PYQ from Mumbai University Dec 2024
✔️ Simple explanation for exams
✔️ Perfect for last-minute revision
✔️ Helpful for IT Sem 6 students
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📌 More Help for You
🔹 Complete DMBI PYQ Playlist (2023–2024) available on the channel
🔹 All varieties of numerical & theory questions covered
🔹 Step-by-step solutions for easy writing in exams
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👍 Like the video
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🔖 Hashtags
#DataMining
#BusinessIntelligence
#DMBI
#MumbaiUniversity
#Sem6IT
#ITEngineering
#PYQ
#Dec2024Exam
#NaiveBayes
#ConfusionMatrix
#AccuracyPrecisionRecall
#DataPreprocessing
#Binning
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#apriori
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