Category Archives: Python Projects

Pneumonia Prediction Using chest x-ray Image Machine Learning

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Chest x-ray: An x-ray exam will allow your doctor to see your lungs, heart and blood vessels to help determine if you have pneumonia. When interpreting the x-ray, the radiologist will look for white spots in the lungs (called infiltrates) that identify an infection. Build an algorithm to automatically identify whether a patient is suffering from pneumonia or not by looking at chest X-ray images. The algorithm had to be extremely accurate because lives of people is at stake. This is a Flask web app designed to analyze a chest x-ray and predict whether a person has TB/pneumonia or not.

Models : 

The model is based on a  convolutional neural network that has been trained on a dataset of 800 images from two sources

The model has an overall accuracy of 83% and an F1 score of 80%.

A negative prediction means that the chest X-ray is most likely normal while the contrary is implied by a positive prediction

Environment and tools

  1. flask
  2. tensorflow

Runtime Python Version  : python-3.8.2

Datasets Link

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Credit Card Fraud Detection Machine Learning Project

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Frauds in mastercard transactions are common today as most folks are using the mastercard payment methods more frequently. this is often thanks to the advancement of Technology and increase in online transaction leading to frauds causing huge loss . Therefore, there's need for effective methods to scale back the loss. additionally , fraudsters find ways to steal the mastercard information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack then on. This paper aims in using the multiple algorithms of Machine learning like support vector machine (SVM), k-nearest neighbor (Knn) and artificial neural network (ANN) in predicting the occurrence of the fraud. Further, we conduct a differentiation of the accomplished supervised machine learning and deep learning techniques to differentiate between fraud and non-fraud transactions.

link of dataset=https://www.kaggle.com/mlg-ulb/creditcardfraud

The datasets contains credit card transactions over a two day collection period in September 2013 by European cardholders. There are a total of 284,807 transactions, of which 492 (0.172%) are fraudulent.

The dataset contains numerical variables that are the result of a principal components analysis (PCA) transformation. This transformation was applied by the original authors to maintain confidentiality of sensitive information. Additionally the dataset contains Time and Amount, which were not transformed by PCA. The Time variable contains the seconds elapsed between each transaction and the first transaction in the dataset. The Amount variable is the transaction amount, this feature can be used for example-dependant cost-senstive learning. The Class variable is the response variable and indicates whether the transaction was fraudulant.

The dataset was collected and analysed during a research collaboration of Worldline and the Machine Learning Group of Université Libre de Bruxelles (ULB) on big data mining and fraud detection.

Models

  • Applied various classification techniques like :-
  • Logistic Regression Light
  • GBM K Nearest Neighbors (KNN ) Classification
  • Trees Random Forest
  • SVM XGBoost Classifier

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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  3. We offer Paid Customization installation Support
  4.  If you have any questions please contact  Support Section
  5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
  6. You can download the product after the purchase by a direct link on this page.

 

Hypo Thyroid Disease prediction Machine Learning Project

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Hypo Thyroid Disease prediction Machine Learning Project

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Hypothyroid diseases (underactive thyroid) is a condition in which the body doesn't produce enough of important thyroid hormones. The condition may lead to various symptoms at late ages. More information about the disease is available at https://www.mayoclinic.org/diseases-conditions/hypothyroidism/symptoms-causes/syc-20350284 .

The Data

The data was from: http://archive.ics.uci.edu/ml/datasets/thyroid+disease. I used "allhypo.data" for the analysis. "allhypo.names" contains the column names of the data. Include the info about primary data processing in the Jupyter notebook list below.

set of algorithms performed to carry out the analysis of the "thyroid-disease" database published in the UCI page
URL data source
data: https://archive.ics.uci.edu/ml/machine-learning-databases/thyroid-disease/sick-euthyroid.data
names: https://archive.ics.uci.edu/ml/machine-learning-databases/thyroid-disease/sick-euthyroid.names

Algorithms

  • Naıve Bayes
  • KNN
  • ANN
  • Random Forest
  • SVM
  • FSF
  • PCA
  • LCA

Related sources

Ionita, Irina. (2016). Prediction of Thyroid Disease Using Data Mining Techniques. BRAIN. Broad Research in Artificial Intelligence and Neuroscience. Vol.7. pp.115-124.
URL: https://www.researchgate.net/publication/321145710_Prediction_of_Thyroid_Disease_Using_Data_Mining_Techniques

Ammulu K., Venugopal. (2017). Thyroid Data Prediction using Data Classification Algorithm. IJIRST –International Journal for Innovative Research in Science & Technology. Vol.4. Issue 2. July 2017. ISSN (online): 2349-6010
URL: http://www.ijirst.org/articles/IJIRSTV4I2054.pdf

Geetha K., Santosh S. Eficient Thyroid Disease Classification Using Differential Evolution with SVM. Journal of Theoretical and Applied Information Technology. Vol.88. No.3. E-ISSN: 1817-3195
URL: http://www.jatit.org/volumes/Vol88No3/4Vol88No3.pdf

Banu, Gulmohamed. (2016). Predicting Thyroid Disease using Linear Discriminant Analysis (LDA) Data Mining Technique. Communications on Applied Electronics. 4. 4-6. 10.5120/cae2016651990. URL: https://www.caeaccess.org/research/volume4/number1/banu-2016-cae-651990.pdf

Lou H, Wang L, Duan D, Yang C,Mammadov M (2018) RDE: A novel approach to improve the classification performance and expressivity of KDB. PLoS ONE 13(7): e0199822. URL: https://doi.org/10.1371/journal.pone.0199822

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  1. One Time Free Installation Support.
  2. Terms and Conditions on this page: https://projectworlds/terms
  3. We offer Paid Customization installation Support
  4.  If you have any questions please contact  Support Section
  5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
  6. You can download the product after the purchase by a direct link on this page.

GST Billing Project in Python Django with Source Code

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GST invoice is a bill or receipt of items sent or services that a seller or service provider offers to a customer. It specifically lists out the services/products, along with the total amount due. One can check a GST invoice to determine said product or service prices before CGST and SGST are levied on them.

What is a GST bill? As a GST registered dealer, you are required to provide GST Invoices, also known as bills to your clients. An invoice or a bill is a list of goods sent or services provided, along with the amount due for payment. You can create GST compliant invoices FREE of cost using ClearTax Billing Software.

Simplest GST Billing Project in Python Django .

Features :

  • Easily create invoices
  • Manage inventory
  • Keep books and track balances
  • Print Bill.
  • Automatic Tax Calculation.
  • Automatic Gst Calculation.

Installation Steps: 

  • cmd -  virtualenv -p python3 venv
  • cmd-  source venv/bin/activate
  • cmd-  pip install -r requirements.txt
  • cmd  - python manage.py migrate
  • cmd  -python manage.py runserver

Download Link

 

Covid-19 Hospital Management python django Project with Source Code

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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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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.

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

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

 

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

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.

python 3.6.8