Prediction

Duration: 2 min

This video lesson is available to enrolled students.

Return to /learn/HPSC-PGT-COMPUTER-SCIENCE/software-engineering/data-warehousing-mining/advanced-data-mining-methods/asset-prediction after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. HPSC Interview Preparation: Interview Preparation
  2. Python Language: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis, DSA using Python
  3. Data Structure & Algorithms: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Algorithm Analysis, Sorting Algorithms, Minimum Spanning Trees, Time Complexity Analysis, Graph Theory, Shortest Path Algos
  4. Computer Networks: Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Security
  5. Computer System & Organisation: Basics of COA, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Control Unit Design, Programming the Basic Computer
  6. Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, SQL, File Organization & Indexing, Transaction Management, Concurrency Control
  7. Operating Systems: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Deadlock, Threads & Process Creation, Memory Management, Virtual Memory, Disc Scheduling
  8. Digital Logic & Circuits: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation
  9. Theory Of Computation: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Pushdown Automata & CFG, Turing Machines, Moore & Mealy Machines
  10. Compiler Design: 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
  11. Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management, Data Warehousing & Mining
  12. Introduction to Emerging Trends: Emerging Tech Trends
  13. Digital Society & Cyber Law: Society, Law & Ethics
  14. HPSC PGT Previous Year Paper:
  15. HPSC Assistant Professor Previous Year Paper:
  16. Live Sessions: HPSC Live Sessions, EMRS TGT/PGT Live Class
  17. Full Mock Test:
  18. HPSC PGT - MOCK Test Series:
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…