Tag Archives: API integration

Online Taxi Booking Python Django with Real time Map

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An online taxi booking Python Django project is a web-based application that allows users to book a taxi ride online through a user-friendly interface. The application integrates with real-time maps to show the location of available taxis and their estimated arrival times.

The project involves designing a database using Django's built-in ORM to store user and ride data. The application's frontend is created using HTML, CSS, and JavaScript to provide an interactive user interface for booking rides, tracking ride progress, and paying for rides.

The Python code interacts with the database to retrieve user data, such as name, email, and payment information, and with real-time map APIs to display the location of available taxis and their estimated arrival times. It uses algorithms to match user requests with available taxis, manage ride progress, and calculate fare amounts.

Some key features of an online taxi booking Python Django project may include:

  1. User registration and login
  2. Ride booking and tracking
  3. Real-time map integration
  4. Payment processing and receipt generation
  5. Ride history and user reviews
  6. Driver and vehicle management
  7. Ride cancellation and refund management
  8. Mobile app integration

Overall, an online taxi booking Python Django project provides a convenient and user-friendly way for users to book and track taxi rides, while providing drivers with a simple way to manage their bookings and pickups. The real-time map integration helps to minimize wait times and improve the overall user experience.

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. 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 3.7.0, 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.

Installation Step : -

  1. python 3.6.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python manage.py runserver

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