Online Pizza Ordering System in Python Django

Buy Source Code ₹701

Welcome to our Online Pizza Ordering System, where you can satisfy your pizza cravings with just a few clicks! This system is designed to streamline the pizza ordering process, offering a user-friendly interface for customers and efficient order management for administrators.

Features:

Pizza Menu:

  • Explore our diverse pizza menu featuring a variety of mouth-watering options. Each pizza comes with a detailed description and price, making it easy for users to choose their favorite flavors.

Order Placement:

  • With a simple click, users can place their pizza orders directly from the menu. The system records the selected pizza, generates a unique order ID, and timestamps the order placement.

Order Status Tracking:

  • Keep tabs on your pizza order with our real-time order status tracking. Users can visit the "Order Status" section, enter their order ID, and view essential information such as the pizza details, order timestamp, and whether the order is completed or pending.

Admin Panel:

  •  The system empowers administrators with a dedicated admin panel to efficiently manage incoming orders. Admins can mark orders as completed and maintain an organized view of both completed and pending orders.

How to Use:

Explore the Menu:

  •  Visit the "Pizza Menu" section to explore our delightful pizza options.
  •  Each pizza is accompanied by its name, description, and price.

Place Your Order:

  •  Click on the "Order" link next to your preferred pizza to initiate the order placement process.
  • Your order is assigned a unique ID, and the system records the timestamp.

Track Your Order:

  • Navigate to the "Order Status" section.
  •  Enter your order ID to retrieve real-time updates on your pizza order, including its current status.

Admin Functions:

  • Admins can access the admin panel to view and manage all incoming orders.
  •  Mark orders as "Completed" once they are ready for delivery or pickup.

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 2.7, 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 3.7 Or Higher
  2. Install Django version 2.2.0
  3. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  4. Finally run cmd - python manage.py runserver

Doctors Appointment System Django Project with source code

Buy Source Code ₹501

The doctor appointment booking website project aimed to create a user-friendly platform for scheduling medical appointments efficiently. Leveraging modern web technologies such as HTML, CSS, JavaScript, and Django, the website offers a seamless experience for both patients and healthcare providers.

A website built using Django, HTML, CSS and JavaScript that enables booking an appointment with a doctor easily.

It features three modules: Admin Module, Doctor Module, Patient Module

A. The Admin Module

  1. Log In
  2. Verify and approve the patient and doctor accounts created.
  3. View the details of the patient as well as the doctor.
  4. Confirm the appointments booked by the patient.
  5. Generate an Invoice.

B. The Doctor Module

  1. Log In/Sign Up
  2. View the details of the patient (symptoms, name, mobile) assigned to them by admin.
  3. View their Appointments, booked by admin.

C. The Patient Module

  1. Log In/Sign Up
  2. View assigned doctor's details like (specialization, mobile number).
  3. View their booked appointment status (pending/confirmed) by admin.
  4. Book appointments.
  5. View/download Invoice pdf.

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

Django Installation Steps :-

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

Blog Website Project Using Python Django Project

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

Download Link

Online Taxi Booking Python Django with Real time Map

Buy Now ₹6501

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

Crop Recommendation using Random Forest flask web app

Buy Now ₹1501

Buy Now Project Report ₹1001

The Crop Recommendation Flask Web App is a web application that recommends the best crop to grow based on soil and climate conditions. The project involves building a machine learning model that can predict the crop yield based on several parameters such as soil pH, temperature, rainfall, humidity, and crop type. The machine learning model is then integrated into a Flask web application to provide farmers with a simple and easy-to-use tool for crop selection.

Here's a general overview of the project:

  1. Data collection: Collect soil and climate data from reliable sources such as the National Soil Information System and the National Oceanic and Atmospheric Administration (NOAA).
  2. Data preprocessing: Clean and prepare the data for use in the machine learning model.
  3. Feature selection: Select the most important features that can affect the crop yield, such as soil pH, temperature, rainfall, humidity, and crop type.
  4. Model training: Train a machine learning model using the preprocessed data and the selected features.
  5. Model evaluation: Evaluate the performance of the machine learning model to ensure it can accurately predict the crop yield.
  6. Flask app development: Develop a Flask web application that allows users to input soil and climate parameters and get a recommendation for the best crop to grow.
  7. Deployment: Deploy the web application to a server so that it can be accessed by users.

Overall, the Crop Recommendation Flask Web App project can be a valuable tool for farmers to increase their crop yield and improve their farming practices.

 Algorithm :

  1. *Random Forest Classifier* is used for development of model.
  2. Only three algorithms are used to predict the output. They are *Logistic Regression*, *XGBoost* and *Random Forest*.\
    1. Accuracy of the model using Logistic Regression is 95%.
    2. Accuracy of the model using Random Forest Classifier is 99%.
    3. Accuracy of the model using XGBoost Classifier is 99%.

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

Fire Detection Using Surveillence Camera web app Project with Source Code

Buy Now ₹1501

Introduction:

The objective of this project is to develop a web application that uses surveillance cameras to detect fire and alert users in real-time. The application uses computer vision algorithms and machine learning techniques to analyze video footage from the cameras and detect the presence of fire. The project aims to improve fire safety by detecting potential fire hazards early and allowing users to take appropriate action.

Methods:

The project involved several steps, including collecting and labeling a dataset of video footage that contained both fire and non-fire events, preprocessing the video footage to extract individual frames, and training a machine learning model using the preprocessed dataset. The machine learning model was a convolutional neural network (CNN) that was trained to detect the presence of fire in an image.

Once the machine learning model was trained, a web application was developed that allowed users to upload video footage from their surveillance cameras. The uploaded footage was analyzed frame by frame using the trained machine learning model to detect the presence of fire. If fire was detected, the application triggered an alert and notified the user via email or SMS. The application also provided a live video feed from the surveillance camera and highlighted the region where the fire was detected.

Results:

The developed web application was able to accurately detect the presence of fire in video footage from surveillance cameras. The machine learning model achieved an accuracy of over 95% on the test dataset, indicating that it was able to accurately distinguish between fire and non-fire events. The web application was also able to provide real-time alerts and notifications to users when fire was detected, allowing them to take appropriate action.

Discussion:

The developed web application has several potential applications in improving fire safety in buildings. For example, it can be used in warehouses, factories, and other industrial settings where fire hazards are common. The application can also be used in homes and other residential settings, alerting residents to potential fire hazards in real-time.

The project has several limitations that should be considered. One limitation is the need for high-quality video footage from surveillance cameras. The accuracy of the machine learning model is highly dependent on the quality of the video footage. Another limitation is the need for periodic retraining of the machine learning model to ensure that it continues to accurately detect fire over time.

Conclusion:

The project has demonstrated the feasibility of using surveillance cameras and machine learning algorithms to develop a web application for fire detection. The application has the potential to improve fire safety in various settings, including industrial and residential settings. Further research is needed to optimize the accuracy of the machine learning model and to develop additional features that can enhance the functionality of the application.

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

Iris Flower Classification with Decision Trees Web App

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

Download

Detecting Fraudulent Transactions using Random Forest Project Proposal

Project Title: Detecting Fraudulent Transactions using Random Forest

Project Description: The objective of this project is to develop a machine learning model using Random Forest to detect fraudulent transactions. Fraudulent transactions can cause significant financial losses to organizations, and machine learning models can help identify such transactions in real-time.

As a student, you can start by collecting a dataset of transactions that includes both legitimate and fraudulent transactions. You can then preprocess the data, perform exploratory data analysis, and engineer relevant features that may help the model identify fraudulent transactions.

You can then use Random Forest, an ensemble learning method that combines multiple decision trees, to build a model that can learn the patterns of fraudulent transactions. You can train the model on the labeled dataset and evaluate its performance using metrics such as accuracy, precision, recall, and F1 score.

Once the model is trained and tested, you can deploy it in a real-time environment using web technologies such as Flask or Django. The model can be integrated into an application that can monitor transactions and flag any that are deemed suspicious.

The final deliverable can be a report that details the methodology, findings, and recommendations for the field of application.

Expected Deliverables:

  1. A detailed analysis of the transaction dataset
  2. A machine learning model using Random Forest to detect fraudulent transactions
  3. An evaluation of the model's performance using metrics such as accuracy, precision, recall, and F1 score
  4. A web application that can flag fraudulent transactions in real-time
  5. A comprehensive report that details the methodology, findings, and recommendations for the field of application.

Tools and Technologies:

  1. Python
  2. Scikit-learn
  3. Pandas
  4. NumPy
  5. Flask or Django

Project Timeline: As a student project, the timeline can be flexible and depend on your availability. However, you can follow this timeline:

  1. Week 1: Understanding fraud detection and transaction datasets
  2. Week 2-3: Data Collection and Preprocessing
  3. Week 4-5: Model Development and Training
  4. Week 6-7: Model Evaluation and Deployment
  5. Week 8: Report Writing and Presentation.

Automated Answer Grading System machine learning project

Buy Source Code ₹1501

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