Tag Archives: User authentication

Blog Website Project Using Python Django Project

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The project aimed to develop a fully functional blog website using the Django web framework. Key features included user authentication, CRUD operations for managing blog posts, a commenting system, rich text editing capabilities, responsive design, and pagination. By leveraging Python, Django, HTML/CSS, and SQLite, the project provided valuable insights into web development. Future improvements were suggested, such as implementing search functionality, categories and tags, social media integration, security enhancements, and performance optimization. Overall, the project offered hands-on experience in building dynamic web applications with Django, empowering users to share their ideas and stories effectively.

Features:

  1. User Authentication: Registration, login, and logout functionalities.
  2. CRUD Operations: Users can create, read, update, and delete blog posts.
  3. Commenting System: Users can comment on posts.
  4. Responsive Design: Ensured the website is accessible across devices.
  5. Pagination: Implemented pagination for listing posts.
  6. Blogs are updated and deleted only by author of that blog.
  7. Profile is only changed by logging user.

Technology Used in the project

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. SQLite : SQLite database has been used as database for the project
  7. Django : Project has been developed over the Django Framework

Supported Operating System

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.10, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Project on Django Installation Steps :-

  1. Install Python 3.10
  2. Install Django version 4.1.6
  3. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  4. Finally run cmd - python manage.py runserver

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Automated Answer Grading System machine learning project

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An Automated Answer Grading System is a machine learning-based Django project that allows teachers to automatically grade student answers in a fast and efficient manner. The system will use natural language processing techniques to analyze and compare the student's answer to the correct answer and assign a grade based on how closely the two match.

The project will consist of a web-based interface that teachers can use to upload student answers and view the results. Teachers will also have the ability to view detailed reports on student performance, including overall scores and breakdowns of individual question scores.

The system will be trained using a dataset of correct and incorrect answers, which will be used to develop the machine learning model that will be used to grade the student's answers. The model will use various natural language processing techniques such as text similarity, sentiment analysis, and topic modeling to compare the student's answer to the correct answer.

The project will be built using the Django web framework and will be hosted on a cloud platform such as AWS or Google Cloud. The frontend of the system will be designed using HTML, CSS, and JavaScript and will provide an easy-to-use and intuitive interface for teachers to interact with.

Overall, the Automated Answer Grading System will be a powerful tool for teachers that will allow them to grade student answers quickly and accurately, freeing up more time for other important teaching tasks.

Dataset

The dataset used is the Kaggle’s Automatic Essay Scoring dataset,can be downloaded from https://www.kaggle.com/c/asap-aes/data

Results

The models were tested using kappa statistic which is intending to compare labelling by different human annotators, not a classifier versus a ground truth. The kappa score is a number between -1 and 1. Scores above .8 are generally considered good agreement,zero or lower means no agreement For this project we have used an Algorithm in which we Combine all the topics into a single model and predicted the score using bi-directional LSTM. kappa score obtained is 0.74