Prediction

Duration: 2 min

This video lesson is available to enrolled students.

Return to /learn/NIACL-AO-IT-SPECIALIST/dbms-it-specialist/data-warehousing-mining/advanced-data-mining-methods/asset-prediction after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. Quantitative Aptitude: Ratio and Proportion (Ratios), Data Interpretation, Mixture and Alligation, Permutation and Combination, Number System, Sequence and Series (Series), Speed Time and Distance, Speed Mathematics and Basic Mathematics, Algebra, Percentage, Profit and Loss, Simple Interest and Compound Interest, Powers and Exponents (Surds and Indices), Time and Work, Mensuration and Geometry, Partnership
  2. Reasoning Ability: Series (Number and Letter Series) (Numerical Relations and Reasoning), Coding Decoding, Data Sufficiency, Seating Arrangements, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Directions (Direction Test), Ranking, Order and Sequence, Syllogisms, Blood Relations, Puzzle Solving (Puzzles), Inequality, Machine Input-Output
  3. English Language: Vocabulary, Noun, Subject Verb Agreement (Verb Noun Agreement), Adjectives, Tenses, Comprehension / Reading Comprehension / Unseen Passages (Critical Reasoning) (Paragraph Questions), Cloze Test (Cloze Passage), Sentence Correction (Error Correction), Sentence Completion (Fill in the blanks), Sentence Re-arrangements (Para Jumbles) (Narrative Sequencing), Sentence Construction, Pronoun, Preposition, Conjunction, Adverb, Verb, Modals, Articles, Determiner, Interjection
  4. General Awareness: Evolution of Indian Banking (Banking & Financial Awareness), GK- Current Affairs, Static General Knowledge, Indian Economy
  5. C Concepts + MCQs (IT Specialist): C Programming by Yash Sir, C Language Core
  6. Java (IT Specialist): Java
  7. Python (IT Specialist): Python for Placements by Yash Sir, Python Basics
  8. OOPs (IT Specialist): Object Oriented Programming (OOPS)
  9. DBMS (IT Specialist): Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, Relational Calculus, Transaction Management, Concurrency Control, Database Recovery, ORDBMS, Database Security & Authorization, Query Processing & Optimization, Enhanced Data Models, Data Warehousing & Mining, Big Data Systems, NoSQL
  10. SQL (IT Specialist): SQL
  11. Software Engineering (IT Specialist): Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  12. Data Structure (IT Specialist): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  13. Operating System (IT Specialist): Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Deadlock, Threads & Process Creation, Memory Management, Virtual Memory, Disc Scheduling, File Management, Windows OS, Linux OS, Security, Distributed Systems, Virtual Machines
  14. Compiler Design (IT Specialist): Intro to Compilers, Lexical Analysis, Grammar & CFG, Syntax Analysis: Top-Down, Syntax Analysis: Bottom-Up, Semantic Analysis & SDT, Intermediate Code Gen, Code Optimization, Run Time Environment
  15. Computer Architecture (IT Specialist): Basics of COA, Floating Point Rep, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Control Unit Design, Register Transfer and Microoperations, Multiprocessors, Programming the Basic Computer
  16. Data Communication & Networking (IT Specialist): Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Security
  17. Descriptive Test (Main Exam): English Writing Ability
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video presents a lecture on prediction in data mining, beginning with a slide titled "What Is Prediction?". It explains that prediction is similar to classification but differs in that it models continuous-valued functions, whereas classification predicts categorical class labels. The major method for prediction is regression, which is further broken down into linear and multiple regression. The lecture then transitions to a new slide, "Regress Analysis and Log-Linear Models in Prediction", which details the mathematical formulas for linear regression (Y = α + βX) and multiple regression (Y = b0 + b1X1 + b2X2). The instructor explains that the parameters in these models are estimated using the least squares criterion, and that nonlinear regression can fit a wide variety of curves.

Chapters

  1. 0:00 – 1:42 00:00-01:42

    The video starts with a slide titled "What Is Prediction?". The instructor explains that prediction is similar to classification, involving constructing a model and using it to predict unknown values. The key difference is that prediction models continuous-valued functions, while classification predicts categorical class labels. The major method for prediction is regression, which includes linear and multiple regression, as well as non-linear regression. The slide then transitions to a new one titled "Regress Analysis and Log-Linear Models in Prediction". This slide defines linear regression as Y = α + βX, where α and β are parameters to be estimated using the least squares criterion. It also defines multiple regression as Y = b0 + b1X1 + b2X2 and states that many nonlinear functions can be transformed into this form. The instructor writes on the slide, adding the equation X + Y = 2 and the text "curve - equation" to illustrate the concept of fitting a curve to data.

The lecture provides a foundational understanding of prediction in data mining by first distinguishing it from classification. It establishes regression as the primary technique for prediction, outlining the core concepts of linear and multiple regression. The progression from the general concept of prediction to the specific mathematical models of regression demonstrates a clear pedagogical structure, moving from high-level definitions to concrete formulas and estimation methods.

Loading lesson…