Tag Archives: CSS

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

Online Time Table Generator PHP MYSQL

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A timetable generator PHP MySQL project is a web-based application that generates a schedule of classes for a school or university based on the available courses, teachers, and classrooms. The application helps to reduce the time and effort required to manually create a timetable and minimize scheduling conflicts between classes, teachers, and classrooms.

The project typically involves designing a database using MySQL to store the necessary information such as course details, teacher information, classroom availability, and scheduling time slots. The application's frontend is created using HTML, CSS, and JavaScript to create an interactive user interface for managing the information.

The PHP code interacts with the MySQL database to retrieve the necessary data to generate the timetable. It uses algorithms to schedule classes and manage conflicts, and displays the final timetable in a user-friendly format, typically an HTML table.

Some key features of a timetable generator PHP MySQL project may include:

  1. Ability to add, edit, and delete courses, teachers, and classrooms
  2. Ability to define scheduling time slots and the duration of each class
  3. Ability to automatically schedule classes and manage conflicts such as double-booked classrooms or teachers
  4. Ability to generate different views of the timetable, such as weekly or monthly views
  5. Ability to print the timetable or export it to other formats such as PDF or Excel

Overall, a timetable generator PHP MySQL project helps to automate the process of creating and managing schedules for educational institutions, thereby saving time and effort while improving efficiency and reducing scheduling conflicts.

Users Roles :

  1. Admin
  2. Teacher/Consultant/Faculty
  3. Student

Admin : The page require user id and password to start the application.

Login is a process by which individual access to a computer system is controlled by identifying and authenticating the user through the cardinalities presented by the user.

Admin can add or delete the category, subcategory etc.

Teacher : Staff can register by admin.

The staff have to login to get more information about the time schedule Dashboard.

Student: Student can register the account by clicking on new register.

He/she can add the account for the various Courses.

The student have to login to get more information about the time schedule.

Brief overview of the technology

Front end: HTML, CSS, JavaScript

  1. HTML: HTML is used to create and save web document. E.g. Notepad/Notepad++
  2. CSS : (Cascading Style Sheets) Create attractive Layout
  3. Bootstrap : responsive design mobile freindly site
  4. JavaScript: it is a programming language, commonly use with web browsers.

Back end: PHP, MySQL

  1. PHP: Hypertext Preprocessor (PHP) is a technology that allows software developers to create dynamically generated web pages, in HTML, XML, or other document types, as per client request. PHP is open source software.
  2. MySQL: MySql is a database, widely used for accessing querying, updating, and managing data in databases.

Software Requirement(any one) 

  1. WAMP Server
  2. XAMPP Server
  3. MAMP Server
  4. LAMP Server
  5. Xamp PHP 5.5 download link -  Click Here

How to Run

Requirements

  1. Download and Install any local web server such as XAMPP/WAMP.
  2. Download the provided source code zip file. (download button is located below)

Installation/Setup ( Note : Watch Above Demo Video to  Underatand )

  1. Open your XAMPP/WAMP's Control Panel and start the Apache and MySQL.
  2. Extract the downloaded source code zip file.
  3. If you are using XAMPP, copy the extracted source code folder and paste it into the XAMPP's "htdocs" directory. And If you are using WAMP, paste it into the "www" directory.
  4. Browse the PHPMyAdmin in a browser. i.e. http://localhost/phpmyadmin
  5. Create a new database naming Database Name.
  6. Import the provided SQL file. The file is known as timetable.sql located inside the db folder.
  7. Browse the Online Clothi Store in a browser. i.e. http://localhost/Project Folder Name/ .

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Iris Flower Classification with Decision Trees Web App

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Objective:

To build a web application that can accurately classify Iris flower species based on their sepal and petal characteristics using a Decision Tree machine learning algorithm.

Dataset: The Iris flower dataset, which contains 150 samples of Iris flowers, each with measurements for sepal length, sepal width, petal length, and petal width. The dataset is labeled with the species of each flower: Iris setosa, Iris versicolor, and Iris virginica.

Methodology:

  1. Data Preprocessing: Load the dataset and split it into training and testing sets. Perform feature scaling to normalize the data.
  2. Decision Tree Model Building: Train a decision tree model on the training data using scikit-learn library. Tune the hyperparameters of the model to obtain the best performance.
  3. Web App Development: Use Flask web framework to create a web app that allows users to input the sepal and petal measurements of an Iris flower and displays the predicted species using the trained decision tree model.
  4. Model Interpretation: Interpret the decision tree to gain insights into which features are most important in classifying the Iris flower species.

Tools and Technologies:

  1. Python
  2. scikit-learn
  3. Flask
  4. HTML
  5. CSS
  6. pandas
  7. numpy
  8. matplotlib.

Conclusion:

Decision Trees are a simple yet powerful machine learning algorithm for classification tasks. In this project, we have built a decision tree model to classify Iris flower species with high accuracy and developed a web application that allows users to interactively predict the species of an Iris flower based on its sepal and petal measurements. The web app can be used for real-world applications such as plant identification, environmental monitoring, and plant breeding.

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. Flask: Project has been developed over the Flask 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 3.6.10, 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.

Installation Step : -

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

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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