• Automatic Student performance prediction is a crucial job due to the large volume of data in educational databases.
  • This job is being addressed by educational data mining (EDM).
  • EDM develop methods for discovering data that is derived from educational environment. These methods are used for understanding student and their learning environment.
  • The educational institutions are often curious that how many students will be pass/fail for necessary arrangements.
  • In this project, two predictive models have been designed namely students’ assessments grades and final students’ performance.
  • The models can be used to detect the factors that influence students’ learning achievement in Machine Learning.
  • The result shows that both models gain feasible and accurate results.


  • In Existing a predictive model for student’s performance by classifying students into binary class (successful / unsuccessful).
  • The proposed model was constructed under the CRISP-DM (Cross Industry Standard Process for Data Mining) approach.
  • The classification algorithm were applied on the given dataset.
  • The model was unable to work out for data high dimensionality and class balancing problems.
  • Accuracy Low When Compare to proposed Algorithm


  • An early warning system was proposed to predict the student learning performances during an online course based on their learning portfolios data.
  • The results showed the approaches accompanied by time dependent variables had high accuracy than other approaches which were not included it.
  • The model was not tested on offline mode.
  • The performance might be decreased in offline mode using time dependent attributes.
  • We collected the learning logs of Multiple students attending Computer Science.
  • In this course, the teacher and students used the LMS, the e-portfolio system and the e-book system.
  • The students were required each week to submit a report, to answer a quiz, to write a logbook of a lecture, and to read slides for preview and review using the three systems.
  • The logs of these learning activities were automatically graded by the system based on the criteria.



Software Requirements:

  • Front End – Anaconda IDE
  • Backend – SQL
  • Language – Python 3.8

Hardware Requirements

  • •Hard Disk: Greater than 500 GB
  • •RAM: Greater than 4 GB
  • •Processor: I3 and Above

Including Packages


  • * Base Paper
  • * Complete Source Code
  • * Complete Documentation
  • * Complete Presentation Slides
  • * Flow Diagram
  • * Database File
  • * Screenshots
  • * Execution Procedure
  • * Readme File
  • * Addons
  • * Video Tutorials
  • * Supporting Softwares

Specialization =======================

  • * 24/7 Support * Ticketing System
  • * Voice Conference
  • * Video On Demand 
  • * Remote Connectivity
  • * Code Customization
  • * Document Customization 
  • * Live Chat Support

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