Subjective Answer Evaluation using Machine Learning


  • This project proposes a novel approach that utilizes various machine learning, natural language processing techniques, to evaluate descriptive answers automatically.
  • Solution statements and keywords are used to evaluate answers, and a machine learning model is trained to predict the grades of answers.
  • With enough training, the machine learning model could be used as a standalone as well.
  • Experimentation produces an accuracy of 97% with the Proposed model.
  • Interestingly, artificial intelligence is utilized extensively as an efficient tool for predicting such a problem.
  • The proposed work utilizes the deep learning technique along with some preprocessing steps to improve the prediction of Answer Evaluation.


  • Much work has been done on the topic of subjective answers evaluation in one form or another, such as measuring Similarity between different texts, words, and even documents.
  • Finding the context behind the text and mapping it with the solution’s context, counting the noun-phrase in the documents, matching keywords in the answers, and so on.


  • Existing studies tend to Miss synonym Errors.
  • Existing studies tend to have an extensive range of possible lengths.
  • Existing studies tend to be randomly ordered among their sentences.


  • This project proposes a new and improved way of evaluating descriptive question answers automatically using machine learning and natural language processing.
  • It uses 2 step approach to solving this problem.
  • First, the answers are evaluated using the solution and provided keywords using various Similarity-based techniques such as word mover’s distance.
  • This form of evaluation by machines is a big step forward in aiding the educational sector to perform their other duties efficiently and reduce the manual labor in trivial tasks such as comparing the answers with a correct solution.



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


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