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Covid-19 Hospital Management python django Project with Source Code

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Latest Python Projects with source code

Covid-19 Hospital, Patient Management, Beds Management, Python Django Project with Source Code . Covid-19 Hospital patient management , system will full and advanced features. Hospital Management System is a web application for the hospital which manages doctors and patients. In this project, we use Python Django  and SQLite database. A responsive web-app with aesthetic and accessible UI for managing COVID patients of a certain hospital built using Django and Bootstrap framework

Features: Covid-19 Hospital Management python django

 

  1. Clean aesthetic User Interface, which changes dynamically as per the status of patient changes
  2. In bed availability grid the red color indicates that bed is occupied else available
  3. It is one of the two pages available for public to view
  4. Here you can add patient for storing it to the db.
  5. Patient's relative contact details are also taken to check if the relative has contacted COVID virus
  6. Bed numbers which are available are only shown
  7. Information filled here, will make changes in dashboard dynamically
  8. Since there are relatively more COVID patients than any other viruses/diseases, a checklist for COVID symptoms only is present
  9. Here you can search patients wrt to name, bed no. doctor assigned and status
  10. You can also find update button to update the patient details.
  11. This is where the actual updates for individual patients are done
  12. Seat Management.
  13. Equipment Management.
  14. Oxygen Management.
  15. Doctor Management.
  16. Daily Patient Routine Checkup .
  17. Hospital capacity, including information on ICU capacity and available ventilators
  18. Staffing levels, including any shortages
  19. How many patients are coming into the hospital with confirmed or suspected COVID-19 cases
  20. Many other relevant details that public health officials need to properly coordinate COVID responses

Technical Specification:

  1. Frontend
  2. HTML5
  3. CSS3
  4. JQuery
  5. Bootstrap4
  6. Backend
  7. Django framework
  8. Database
  9. SQLite

Installation

  • Setup virtual environment
  • Exceute pip install -r requirements.txt.
  • run python manage.py runserver.

Download Link 

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Live Face Mask Detection Project in Machine Learning

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Latest Machine Learning Project with Source Code

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Face Mask Detection web applicaion built with Flask, Keras-TensorFlow, OpenCV. It can be used to detect face masks both in images and in real-time video.

The goal is to create a masks detection system, able to recognize face masks both in images, both in real-time video, drawing bounding box around faces. In order to do so, I finetuned MobilenetV2 pretrained on Imagenet, in conjunction with the OpenCV face detection algorithm: that allows me to turn a classifier model into an object detection system. Live Face Mask Detection Project in Machine Learning.

Technologies

  • Keras/Tensorflow
  • OpenCV
  • Flask
  • MobilenetV2

Installation:

You have to install the required packages, you can do it:

  • via pip pip install -r requirements.txt
  • or via conda conda env create -f environment.yml

Once you installed all the required packages you can type in the command line from the root folder:

python app.py

and click on the link that the you will see on the prompt.

Datasets

The dataset used for training the model is available here.

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Loan Eligibility Prediction Python Machine Learning Project

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Loan Eligibility Prediction Python Machine Learning Project. Loan approval is a very important process for banking organizations. The system approved or reject the loan applications. Recovery of loans is a major contributing parameter in the financial statements of a bank. It is very difficult to predict the possibility of payment of loan by the customer. In recent years many researchers worked on loan approval prediction systems. Machine Learning (ML)techniques are very useful in predicting outcomes for large amount of data.

Key Features

  • Interface to predict loan application approval
  • data insights withhin Jupyter Notebook
  • Trained Model
  • multiple machine learning algorithms.

Technology :

  • Flask==1.1.1
  • html5lib==1.0.1
  • json5==0.8.5
  • jsonify==0.5
  • numpy==1.16.5
  • pandas==0.25.1
  • scikit-image==0.15.0
  • scikit-learn==0.21.3
  • scipy==1.3.1
  • gunicorn==19.9.0
  • requests==2.22.0
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Loan Defaulter Prediction Machine Learning Projects

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Using supervised machine learning to train a model with credit default data to determine the probability and/or classification (“default” vs “non-default”) of the user’s liability. The UI will take user input such as, such as education level, sex, marital status, payment history and income, and will return a classification.

An app like this would be useful for financial and lending institutions to understand and manage the risk of their loans and lending portfolios.

 

Goals/Outcome

  • Determining probability of user liability
  • Creating an interactive UI that will take users input and return an output
  • To determine if a neural network vs logistic regression is the better model for classification

Models Created

  • Logistic Regression
  • Random Forest Model
  • Deep Neural Network

About

Probability of Credit Card Default, Machine Learning

Technologies Used : -

  • beautifulsoup4==4.6.0
  • certifi==2018.4.16
  • chardet==3.0.4
  • click==6.7
  • Flask==1.0
  • gunicorn==19.8.0
  • idna==2.6
  • itsdangerous==0.24
  • Jinja2==2.10
  • MarkupSafe==1.0
  • numpy==1.14.3
  • pandas==0.22.0
  • python-dateutil==2.7.2
  • pytz==2018.4
  • requests==2.18.4
  • scikit-learn==0.19.1
  • scipy==1.0.1
  • six==1.11.0
  • SQLAlchemy==1.2.7
  • urllib3==1.22
  • Werkzeug==0.14.1

 

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Used Car Price Prediction Using Machine Learning

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Car Price Prediction is a really an interesting machine learning problem as there are many factors that influence the price of a car in the second-hand market. In this competition, we will be looking at a dataset based on sale/purchase of cars where our end goal will be to predict the price of the car given its features to maximize the profit.

Datasets Link - Kaggle Data 

Technologies Used : -

  1. Python 3.7
  2. Pandas
  3. Numpy
  4. Flask

Running the web app

Locally

  • Install requirements
    pip install -r requirements.txt
  • Run flask web app
    python app.py
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Skin cancer Detection using Machine learning

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Skin cancer Detection using Machine learning .The purpose of this project is to create a tool that considering the image of a mole, can calculate the probability that a mole can be malign.

Skin cancer is a common disease that affect a big amount of peoples. Some facts about skin cancer:

Every year there are more new cases of skin cancer than the combined incidence of cancers of the breast, prostate, lung and colon.

An estimated 87,110 new cases of invasive melanoma will be diagnosed in the U.S. in 2017.

The estimated 5-year survival rate for patients whose melanoma is detected early is about 98 percent in the U.S. The survival rate falls to 62 percent when the disease reaches the lymph nodes, and 18 percent when the disease metastasizes to distant organs.

Development process and Data

The idea of this project is to construct a CNN model that can predict the probability that a specific mole can be malign.

Data: Skin cancer Detection using Machine learning

To train this model I'm planning to use a set of images from the International Skin Imaging Collaboration:

Mellanoma Project ISIC https://isic-archive.com.

The specific datasets to use are:

ISICUDA-21: Moles and melanomas. Biopsy-confirmed melanocytic lesions. Both malignant and benign lesions are included.

Benign: 23

Malign: 37

ISICUDA-11 Moles and melanomas. Biopsy-confirmed melanocytic lesions. Both malignant and

benign lesions are included.

Benign: 398

Malign: 159

ISICMSK-21: Benign and malignant skin lesions. Biopsy-confirmed melanocytic and non-melanocytic lesions.

Benign: 1167 (Not used)

Malign: 352

ISICMSK-12: Both malignant and benign melanocytic and non-melanocytic lesions. Almost all images confirmed by histopathology. Images not taken with modern digital cameras.

Benign: 339

Malign: 77

ISICMSK-11: Moles and melanomas. Biopsy-confirmed melanocytic lesions, both malignant and benign.

Benign: 448 Malign: 224

As summary the total images to use are:

Benign ImagesMalign Images
1208849

Some sample images are shown below: 1. Sample images of benign moles:

Sample images of malign moles:

Preprocessing:

The following preprocessing tasks are going to be developed for each image: 1. Visual inspection to detect images with low quality or not representative 2. Image resizing: Transform images to 128x128x3 3. Crop images: Automatic or manual Crop 4. Other to define later in order to improve model quality

CNN Model:

The idea is to develop a simple CNN model from scratch, and evaluate the performance to set a baseline. The following steps to improve the model are: 1. Data augmentation: Rotations, noising, scaling to avoid overfitting 2. Transferred Learning: Using a pre-trained network construct some additional layer at the end to fine tuning our model. (VGG-16, or other) 3. Others to define.

Model Evaluation:

To evaluate the different models we will use ROC Curves and AUC score. To choose the correct model we will evaluate the precision and accuracy to set the threshold level that represent a good tradeoff between TPR and FPR.

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Heart Disease Prediction using Machine Learning Project

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Introduction

Heart diseases is a term covering any disorder of the heart. Heart diseases have become a major concern to deal with as studies show that the number of deaths due to heart diseases have increased significantly over the past few decades in India, in fact it has become the leading cause of death in India.

A study shows that from 1990 to 2016 the death rate due to heart diseases have increased around 34 per cent from 155.7 to 209.1 deaths per one lakh population in India.

Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate.

Problem Description :

A dataset is formed by taking into consideration some of the information of 920 individuals. The problem is : based on the given information about each individual we have to calculate that whether that individual will suffer from heart disease.

Dataset :

The Heart disease data set consists of patient data from Cleveland, Hungary, Long Beach and Switzerland. The combined dataset consists of 14 features and 916 samples with many missing values. The features used in here are,

  1. Age : displays the age of the individual.
  2. Sex : displays the gender of the individual using the following format : 1 = male 0 = female.
  3. Chest-pain type : displays the type of chest-pain experienced by the individual using the following format : 1 = typical angina 2 = atypical angina 3 = non - anginal pain 4 = asymptotic
  4. Resting Blood Pressure : displays the resting blood pressure value of an individual in mmHg (unit)
  5. Serum Cholestrol : displays the serum cholestrol in mg/dl (unit)
  6. Fasting Blood Sugar : compares the fasting blood sugar value of an individual with 120mg/dl. If fasting blood sugar > 120mg/dl then : 1 (true) else : 0 (false)
  7. Resting ECG : 0 = normal 1 = having ST-T wave abnormality 2 = left ventricular hyperthrophy
  8. Max heart rate achieved : displays the max heart rate achieved by an individual.
  9. Exercise induced angina : 1 = yes 0 = no
  10. ST depression induced by exercise relative to rest : displays the value which is integer or float.
  11. Peak exercise ST segment : 1 = upsloping 2 = flat 3 = downsloping
  12. Number of major vessels (0-3) colored by flourosopy : displays the value as integer or float.
  13. Thal : displays the thalassemia : 3 = normal 6 = fixed defect 7 = reversable defect
  14. Diagnosis of heart disease : Displays whether the individual is suffering from heart disease or not : 0 = absence 1,2,3,4 = present.

Technologies Used : -

  1. Python 3.7
  2. Pandas
  3. Numpy
  4. Flask

Running the web app

Locally

  • Install requirements
    pip install -r requirements.txt
  • Run flask web app
    python main_file.py

Models used and accuracy

A Random forest classifier achieves an average multi-class classification accuracy of 56-60%(183 test samples). It gets 75-80% average binary classification accuracy(heart disease or no heart disease).

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Diabetes Prediction using Machine Learning Project Code

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In this Diabetes Prediction using Machine Learning Project Code, the objective is to predict whether the person has Diabetes or not based on various features like Number of Pregnancies, Insulin Level, Age, BMI.The data set that has used in this project has taken from the kaggle . "This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage." and used a simple random forest classifier.

Learning Objectives : -

The following points were the objective of the project (The main intention was to create an end-to-end ML project.)

  1. Data gathering
  2. Descriptive Analysis
  3. Data Visualizations
  4. Data Preprocessing
  5. Data Modelling
  6. Model Evaluation
  7. Model Deployment

Technical Aspect : -

  1. Training a machine learning model using scikit-learn.
  2. Building and hosting a Flask web app.
  3. A user has to put details like Number of Pregnancies, Insulin Level, Age, BMI etc .
  4. Once it get all the fields information , the prediction is displyed on a new page .

Technologies Used : -

  1. Python 3.7
  2. Pandas
  3. Numpy
  4. Flask

 

Datasets

https://www.kaggle.com/uciml/pima-indians-diabetes-database

 

Installation

  1. Download  and unzip it.
  2. After downloading, cd into the flask directory.
  3. Begin a new virtual environment with Python 3 and activate it.
  4. Install the required packages using pip install -r requirements.txt

RUN

  1. Execute the command: python app.py
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Bus Reservation System Project Python Django

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Bus Reservation System is a preety basic system developed in Django,SQLite,Python, which is designed to automate the online ticket purchasing through an easy online bus booking system.With the bus ticket reservation system you can manage/book reservations, clients data and passengers lists through its Django admin and book tickets effortlessly through the Bus reservation Website.

Features

  1. Built with Python 3.6, Djang0 2.0 Framework
  2. Styled using Bootstrap4
  3. Uses SQLite
  4. Sign in with the application to start using.
  5. Set up a profile about and manage your details
  6. Search for buses based on source and destination
  7. Booking buses
  8. Cancel bus reservations
  9. View buses booked and cancelled
  10. View available buses listing
  11. Login , Registration
  12. Admin Panel

Technology Used 

  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

  • We can configure this project on following operating system.
  • Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  • Python 2.7, PIP, Django.
  • 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 Steps :-

  • Install Python 3.7 Or Higher
  • Install Django version 2.2.0
  • Finally run cmd - python manage.py runserver

 

 

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Hostel Management System Project in Python Django

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This particular project deals with the issues on managing a hostel and avoids the issues which occur when carried manually.Identification of the drawbacks of the prevailing system results in the designing of computerized system which will be compatible to the prevailing system with the system Which is more user friendly and more GUI oriented. We can improve the efficiency of the system, thus overcome the drawbacks of the existing system.

Features :

  1. Login System.
  2. View Room Details
  3. Book Hostel
  4. Student Registration
  5. Manage Students.
  6. Billing.
  7. User Management.
  8. Room Management.
  9. Meal management.
  10. Cost Management.
  11. Student Management.
  12. Visitor Management .

Technology Used in the project 

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

Supported Operating System

  • We can configure this project on following operating system.
  • Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  • Python 3.7, PIP, Django 3.1.3 .
  • 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.

Requirements.txt File

  • asgiref==3.3.0
  • Django==3.1.3
  • gunicorn==20.0.4
  • pytz==2020.4
  • sqlparse==0.4.1\
  • whitenoise==5.2.0

Django Installation Steps :-

  • Install Python 3.7 Or Higher
  • Install Django version 3.1.3
  • Install all dependencies cmd -python -m pip install –-user -r requirements.txt
  • Finally run cmd - python manage.py runserver