ABSTRACT

• 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.

EXISTING SYSTEM

• 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

PROPOSED SYSTEM

• 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.

PROJECT VIDEO

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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