Category Archives: Python Projects

Image to Cartoon Python OpenCV Machine Learning

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Image to Cartoon Python OpenCV Machine Learning Free Source Code . This Project web app project you can directly select image then you can convert any image to cartoon . its very interesting project . This is simple and basic level small project for learning purpose. Also you can modified this system as per your requriments and develop a perfect advance level project.

Image to Cartoon Python OpenCV Machine Learning Free Source Code

Required modules in this Projects

  1. CV2: Imported to use OpenCV for image processing
  2. easygui: Imported to open a file box. It allows us to select any file from our system.
  3. Numpy: Images are stored and processed as numbers. These are taken as arrays. We use NumPy to deal with arrays.
  4. Imageio: Used to read the file which is chosen by file box using a path.
  5. Matplotlib: This library is used for visualization and plotting. Thus, it is imported to form the plot of images.
  6. OS: For OS interaction. Here, to read the path and save images to that path.
  7. Flask: Flask is a micro web framework written in Python.

Steps to develop Image to cartoon 

  1. Importing the required modules
  2. Building a File Box to choose a particular file
  3. How is an image stored?
  4. Transforming an image to grayscale
  5. Smoothening a grayscale image
  6. Retrieving the edges of an image
  7. Preparing a Mask Image
  8. Giving a Cartoon Effect
  9. Plotting all the transitions together
  10. Functionally of save or download  button

Application tested on:

  1. python 3.7
  2. tensorflow 2.1.0
  3. tf_slim 1.1.0
  4. ffmpeg 3.4.8
  5. Cuda version 10.1
  6. OS: Windows 10

Algorithmia For Video Convert 

We used the Serveless AI Layer product of Algorithmia for inference on videos. To learn more on how to deploy your model in Algorithmia, check here - https://algorithmia.com/developers

 

Installation Step : -

  1. python 3.7.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Download Source Code

 

Online Book Store Project in Python Django

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This Online Book Store  Project in Django created based on python, Django, and SQLITE3 Database. The Online Book Store System is a simple project similar like shopping cart or ecommerce  but is only for book shopping. Categories wise books available its very good project for Final Year student academic Purpose. This project built with django framework and it's allows users to search and purchase book online based on category, author and subject.

This Online Book Store  Project in Django  Framework, Also includes a Download Source Code for free, just find the downloadable source code below and click download now.

Admin Features of Online Book Store  Project in Django

  1. Dashboard – For the admin dashboard, you will be able to all the basic access in the whole system. Such as summary of products, orders, and the categories.
  2. Manage Books– The admin has access to the books management information system. He can add, update and delete the books.
  3. Manage Categories – The page where the admin can add, edit and delete categories information.
  4. Manage Orders – As the main functions of the admin, the admin can accept or reject the order from the customers on a case to case basis and the list of customer orders are listed.
  5. Manage User– The admin can manage the user’s account. Admin can add, update and Block user in the system.
  6. Login and Logout – By default one of the security features of this system is the secure login and logout system.

Customer Features of Online Book Store  Project in Django

  1. Login Page – Customer enter their website credentials on this page to gain access in order to log in.
  2. Register Page– The page where new customer created their login credentials for the website.
  3. Home Page– When customer visit the website, this is the system’s default page. This page shows the books for sale in the store, or by entering a keyword in the search box above the books.
  4. Book View Page – The page on which the product’s specific information is shown, as well as the page on which the customer adds the product to his or her cart.
  5. Cart List Page– The page that lists the items that customer have chosen. This is the page where the customer can complete the order checkout process.
  6. My Order Page – The page that lists the customer’s orders.
  7. bcash and Credit Card Payments – This Online Book Store  Project in Django in Django has a payment method that uses Paypal and Credit Card Payments.

Installation Steps : Online Book Store  Project in Django

  1. Download and extract File
  2. goto Project directory and open cmd
  3. install Requirement package - python -m pip install –-user -r requirements.txt
  4. run project - python manage.py runserver 

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Insurance Claim Prediction Machine Learning Project with Source Code

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insurance claim prediction machine learning. Insurance companies are extremely interested in the prediction of the future. Accurate prediction gives a chance to reduce financial loss for the company. A major cause of increased costs are payment errors made by the insurance companies while processing claims. Furthermore, because of the payment errors, processing the claims again accounts for a significant portion of administrative costs.

Dataset :

This dataset contains 7 features as shown below:

age: age of the policyholder
sex: gender of policyholder (female=0, male=1)
BMI: Body mass index, providing an understanding of the body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 25
steps: average walking steps per day of the policyholder
children: number of children/dependents of the policyholder
smoker: smoking state of policyholder (non-smoke=0;smoker=1)
region: the residential area of the policyholder in the US (northeast=0, northwest=1, southeast=2, southwest=3)
charges: individual medical costs billed by health insurance.

Installation Steps :-

  1. Install Python 3.7.0
  2. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  3. Finally run cmd - python app.py

Download Link 

Medical Insurance Cost Prediction Project in Python Flask

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Medical Insurance Cost Prediction using Random Forest Regressor.

To predict things have been never so easy. I used to wonder how Insurance amount is charged normally. So, in the mean time I came across this dataset and thought of working on it! Using this I wanted to know how few features determine our insurance amount!

Features

  1. Exploring the dataset
  2. Converting Categorical values to Numerical
  3. Plotting Heatmap to see dependency of Dependent valeu on Independent features
  4. Data Visualization (Plots of feature vs feature)
  5. Plotting Skew and Kurtosis
  6. Data Preparation
  7. Prediction using Linear Regression
  8. Prediction using SVR
  9. Prediction using Ridge Regressor
  10. Prediction using Random Forest Regressor
  11. Performing Hyper tuning for above mentioned models
  12. Plotting Graph for all Models to compare performance
  13. Preparing model for deployment
  14. Deployed model using Flask

Results

Model gave 86% accuracy for Medical Insurance Amount Prediction using Random Forest Regressor

Dataset

The dataset used can be downloaded here (Kaggle) - Click to Download

Installation Steps :-

  1. Install Python 3.7.0
  2. Install all dependencies cmd -python -m pip install --user -r requirements.txt
  3. Finally run cmd - python app.py

 

Download Link 

Breast Cancer Prediction Machine Learning Project Source Code

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Breast cancer is the most common type of cancer in women. When cancers are found early, they can often be cured. There are some devices that detect the breast cancer but many times they lead to false positives, which results is patients undergoing painful, expensive surgeries that were not even necessary. These type of cancers are called benign which do not require surgeries and we can reduce these unnecessary surgeries by using Machine Learning. We take a dataset of the previous breast cancer patients and train the model to predict whether the cancer is benign or malignant. These predictions will help doctors to do surgeries only when the cancer is malignant, thus reducing the unnecessary surgeries for woman.

Models 

Logistic Regression model is developed based on 10 features that classify whether the breast cancer is benign or malignant. For classifying the patient, users are requested to submit their data on this following form as per the value range.

Languages  Used

  • Python: language
  • NumPy: library for numerical calculations
  • Pandas: library for data manipulation and analysis
  • SkLearn: library which features various classification, regression and clustering algorithms
  • Flask: microframework for building web applications using Python.

Installation Steps :-

  • Install Python 3.7.0
  • Install all dependencies cmd -python -m pip install --user -r requirements.txt
  • Finally run cmd - python app.py

 

ONLINE GYM MANAGEMENT SYSTEM IN PYTHON DJANGO

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GYM Management System is based on python Django to provide different type of service through online.it is college submit mini project that help both user as well as gym administrator. Python gym management is final year project to manage and secure by Django database.

Features Of Python Gym Management System

  1. New user contact through website.
  2. User also queries about all doubts.
  3. User Online Check Time schedule of gym
  4. User also check the gym trainer name and his experience.
  5. Admin manage all data
  6. Add user
  7. Add new equipment for gym
  8. Add new trainee details
  9. Update about timing
  10. Create Package for monthly yearly and half quarter
  11. Check queries about user and reply them.
  12. Add new join user create account and provide details.

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.

Online Assignment Submission Project on Django Installation Steps :-

  • Install Python 3.7 Or Higher
  • Install Django version 2.2.0
  • Install all dependencies cmd -python -m pip install --user -r requirements.txt
  • Finally run cmd - python manage.py runserver

 

IMDB Sentiment Analysis based on comment Machine Learning

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his is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training and 25,000 for testing. So, predict the number of positive and negative reviews using either classification or deep learning algorithms.

Computer Vision is the branch of the science of computers and software systems which can recognize as well as understand images and scenes. Computer Vision is consists of various aspects such as image recognition, object detection, image generation, image super-resolution and many more. Object detection is widely used for face detection, vehicle detection, pedestrian counting, web images, security systems and self-driving cars. In this project, we are using highly accurate object detection-algorithms and methods such as R-CNN, Fast-RCNN, Faster-RCNN, RetinaNet and fast yet highly accurate ones like SSD and YOLO. Using these methods and algorithms, based on deep learning which is also based on machine learning require lots of mathematical and deep learning frameworks understanding by using dependencies such as TensorFlow, OpenCV, imageai etc, we can detect each and every object in image by the area object in an highlighted rectangular boxes and identify each and every object and assign its tag to the object. This also includes the accuracy of each method for identifying objects.

Requirements.txt

  1. flasgger==0.9.4
  2. Flask==1.0.3
  3. gunicorn==19.9.0
  4. itsdangerous==1.1.0
  5. Jinja2==2.10.1
  6. MarkupSafe==1.1.1
  7. Werkzeug==0.15.5
  8. numpy==1.18.1
  9. scipy==1.4.1
  10. scikit-learn==0.22.1
  11. matplotlib==3.2.1
  12. pandas==1.0.3
  13. nltk==3.4.5

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 Link

 

 

Salary Prediction using Machine Learning Web App

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Salary Prediction Based on work experience ML Web App. The purpose of this project is to use data transformation and machine learning to create a model that will predict a salary when given years of experience, job type. The purpose of this project is to use data transformation and machine learning to create a model that will predict a salary when given years of experience, job type.

Data The data for this model is fairly simplified as it has very few missing pieces. The raw data consists of a training dataset with the features listed above and their corresponding salaries.

Information Used To Predict Salaries Years Experience: How many years of experience .

This model can be used as a guide when determining salaries since it shows reasonable predictions when given information on years of experience.

Methods Used

  1. Data Analysis and Visualization
  2. Linear Regression
  3. Polynomial Transformation
  4. Ridge Regression
  5. Random Forest

Technologies/Libraries Used

  1. Python 3
  2. Pandas
  3. NumPy
  4. Seaborn
  5. Scikit-learn
  6. Matplotlib
  7. SciPy
  8. Jupyter

Data

The data for this model is fairly simplified as it has very few missing pieces. The raw data consists of a training dataset with the features listed above and their corresponding salaries. Twenty percent of this training dataset was split into a test dataset with corresponding salaries.

There is also a testing dataset that does not have any salary information available and was used as a substitute for real-world data.

Information Used To Predict Salaries

  1. Years Experience: How many years of experience

Overview

  1. This is project predicts the salary of the employee based on the experience.

Model Training :-

    model.py trains and saves the model to the disk.
    model.pkb the pickle model

Run App :-
    app.py contains all the requiered for flask and to manage APIs.

Procedure
Open command Prompt and go to given directory and then run python app.py

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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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