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Online Job portal Project in Python Django with source code

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Latest Python Projects with source code

Online Job portal Project in Python Django with source code. This web application is to be conceived in its current form as a dynamic site-requiring constant updates both from the seekers as well as the companies.
The objective of the project is to enable jobseekers to place their resumes and find appropriate jobs while companies to publish their vacancies and find good candidates.
It enables jobseekers to post their resume, look for jobs, view personal job listings. It will provide various companies to put their vacancy profile on the location and even have an choice to search candidate resumes.
Apart from job-seekers and Companies(Job Provider) there'll be an admin module to manage complete Portal also as jobseeker and corporations .

Online Jop Portal Features

  • Administrator
  • Job Seeker
  • Job Provider
  • Job Search

System Users

  • Administrator
  • Job Seeker
  • Job Provider

ADMINISTRATOR FEATURES

Administrator can manage whole website:

  • Manage complete jobseeker section. Like: activate/deactivate/delete/ edit jobseeker’s information.
  • Admin user can view the jobseeker’s applications for each job.
  • Manage complete employer section. Admin user can activate/deactivate/delete/ edit company information.
  • Manage posted jobs. Like: activate/deactivate/delete/edit posted job.
  • Manage whole website content. Dynamic CMS is included to manage the content of the website.
  • Admin user can send message to any jobseeker or job provider.
  • Admin user can send bulk emails as well.
  • Admin user can manage the skills section. Like: Add or remove skills from the website.
  • Manage newsletters section
  • Manage success stories
  • Admin user can manage and handle the prohibited words for whole website.
  • Admin user can add/edit countries, cities, salaries range, qualification, institutes, job industries, website ads.

JOB PROVIDER / COMPANY FEATURES

After registration job provider can perform following action:

  • Add / Edit company’s profile
  • Post new job vacancies
  • Edit / Deactivate posted jobs
  • Job provider can see the list of jobseekers who has applied for the job
  • Job provider can search jobseekers
  • Job provider can see and download the jobseeker’s resume
  • Job provider can send message to any job seeker

JOB SEEKER FEATURES

After registration job seeker can perform following actions:

  • Search for jobs
  • Apply Online for desire job
  • Add/Edit profile information including qualification, experience, and skills.
  • Build his resume by using CV builder functionality of the website.
  • Upload latest resume.

MAIN WEBSITE(WEBSITE FONT END)

From main website, user can perform following actions:

  • Search jobs on the basis of skills, city, country or job title
  • Register as a jobseeker or as a job provider
  • Login to jobseeker or job provider portal
  • About Us
  • Contact us
  • Recent Jobs

Local environment Install

  1. Clone the repository and install the packages in the virtual env:pip install -r requirements.txt
  2. Add .env file.cp .env.dev.sample .env
  3. Add Github client ID and client secret in the .env file

Run

1.With the venv activate it, execute:

python manage.py collectstatic

Note : Collect static is not necessary when debug is True (in dev mode)

  1. Create initial database:python manage.py migrate
  2. Load demo data (optional):python manage.py loaddata fixtures/app_name_initial_data.json – app app.model_name
  3. Run server:python manage.py runserver

Run test:

python manage.py test

To dump data:

python manage.py dumpdata – format=json – indent 4 app_name > app_name/fixtures/app_name_initial_data.json

 

Download Link 

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Fake Product Review Detection using Machine Learning

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Online reviews play a very important role for decision-making in today's e-commerce. Large parts of the population, i.e. customers read product or store reviews before deciding what to buy or where to buy and whether to buy or not. Because writing fake / fraudulent reviews comes with monetary gain, online review websites there has been a huge increase in tricky opinion spam. Basically, an untruthful review is a fake review or fraudulent review or opinion spam. Positive reviews of a target object can attract more customers and increase sales; negative reviews of a target object can result in lower demand and lower sales. Fake review detection has attracted considerable attention in recent years. Most review sites, however, still do not filter fake reviews publicly. Yelp is an exception that over the past few years

has filtered reviews. Yelp's algorithm, however, is a business secret. In this work, by analyzing their filtered reviews, we try to find out what Yelp could do. The results will be useful in their filtering effort for other review hosting sites. Filtering has two main approaches: supervised and unmonitored learning. There are also about two types in terms of the characteristics used: linguistic characteristics and behavioral characteristics. Through supervised learning approach we have tried to make a model which can identify the fake review with almost 70 percent accuracy.

As the Internet continues to grow in size and importance, the quantity and impact of online reviews is increasing continuously. Reviews can influence people across a wide range of industries, but they are particularly important in e-commerce, where comments and reviews on products and services are often the most convenient, if not the only, way for a buyer to decide whether to buy them.

 

Model training

Refer to the Jupyter notebooks in research folder to know the steps taken for preprocessing, model development and algorithms used. Although we experemented with different models, we found Naive Bayes to be most accurate with F1 score of 77%. 

Installing and running this app:

  1. Requirements: Use pip install/conda install to download following packages
  • Numpy, pandas
  • sklearn
  • spacy
  • Django 2.1
  • pickle
  • tqdm
  1. running the app:
  • Go to folder containing manage.py and run command: python manage.py runserver
  • Once the server starts, open browser. The app runs on http://127.0.0.1:8000/
  • fake_reviews.txt and real_reviews.txt contains some reviews that can be used to test the working of model.
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Fake News Detection using Machine Learning Natural Language Processing

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Fake News Detection using Machine Learning Natural Language Processing . A NLP and Machine Learning based web application used for detecting fake news. Uses NLP for preprocessing the input text. Uses XGBoost model for predicting whether the input news is Fake or Real.

here are tons of stories articles, where the news is fake or cooked up. With numerous advances in tongue Processing and machine learning, we will actually build an ml model which is in a position to detect if a bit of stories ... Here we'll be using artificial neural network models to verify the genuinity of the article.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python3

Numpy , Pandas , Scipy
matplotlib
scikit-learn
seaborn

Database

SQLite

Dataset Link: https://www.kaggle.com/c/fake-news/data

Training Model File 

Fake_News_Classifier_Using_LSTM.ipynb

Fake_News_Classifier_using_Machine_Learning.ipynb

Output Generated File

xgb_fake_news_predictor.pkl

 

 

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Driver Distraction Prediction Using Deep Learning, Machine Learning

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Driver Distraction Prediction Using Machine Learning”, where given driver images, each taken during a car with a driver doing something within the car (texting, eating, talking on the phone, makeup, reaching behind, etc). The goal was to predict the likelihood of what the driving force is doing in each picture.

Driving a car may be a complex task, and it requires complete attention. Distracted driving is any activity that takes away the driver’s attention from the road. Several studies have identified three main sorts of distraction: visual distractions (driver’s eyes off the road), manual distractions (driver’s hands off the wheel) and cognitive distractions (driver’s mind off the driving task).

Dataset details -

  • Image Size - 480 X 640 pixels
  • Training Images count - 22424 images
  • Test Images count - 79726 images
  • Image type - RGB
  • Image field of view - Dashboard images with view of Driver and passenger
  • The 10 classes to predict are:
    • c0: safe driving
    • c1: texting - right
    • c2: talking on the phone - right
    • c3: texting - left
    • c4: talking on the phone - left
    • c5: operating the radio
    • c6: drinking
    • c7: reaching behind
    • c8: hair and makeup
    • c9: talking to passenger
  • Loss - multi-class logarithmic loss

State-Farm-Distracted-Driver-Detection

Kaggle hosted the challenge few years ago which focused on identifying distracted drivers using Computer Vision
Details of challenge can be found here - 
https://www.kaggle.com/c/state-farm-distracted-driver-detection

Implementation Details

  • DL Model - CNN's build from scratch ( 6 Conv Layer, 5 Dropout Layer, 3 Dense Layer)
  • Framework - Keras / Pytorch version in the process.
  • CNN Model Visualization/Model Interpretability - GradCAM
  • Final Accuracy -Train acc - 99.06%, Val acc-99 .46%

GRAD-CAM implementation for a test image with label drinking

GRAD-CAM is a technique to highlight how a model classifies new instanes by creating a heat map which highlights only the area which has contributed the most in prediction.
As seen in below image model classifies driver as distracted by drinking by highlighting the hand and glass.

 

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Multiple Disease Prediction using Machine Learning

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Multiple Disease Prediction using Machine Learning . This Web App was developed using Python Flask Web Framework . The models won’t to predict the diseases were trained on large Datasets. All the links for datasets and therefore the python notebooks used for model creation are mentioned below during this readme. The WebApp can predict following Diseases:

  • Diabetes
  • Breast Cancer
  • Heart Disease
  • Kidney Disease
  • Liver Disease
  • Malaria
  • Pneumonia

Models with their Accuracy of Prediction

DiseaseType of ModelAccuracy
DiabetesMachine Learning Model98.25%
Breast CancerMachine Learning Model98.25%
Heart DiseaseMachine Learning Model85.25%
Kidney DiseaseMachine Learning Model99%
Liver DiseaseMachine Learning Model78%
MalariaDeep Learning Model(CNN)96%
PneumoniaDeep Learning Model(CNN)95%

 

Steps to run the WebApp in local Computer

Step-1: Download the files in the repository.
Step-2: Get into the downloaded folder, open command prompt in that directory and install all the dependencies using following command

pip install -r requirements.txt

Step-3: After successfull installation of all the dependencies, run the following command

python app.py

Dataset Links

All the datasets were used from kaggle.

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Crime Data Analysis Project in Machine Learning

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Crime Data Analysis Project in Machine Learning .Crime analyses is one among the important application of knowledge mining. data processing contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications It can help the analysts to spot crimes faster and help to form faster decisions.
The main objective of crime analysis is to seek out the meaningful information from great deal of knowledge and disseminates this information to officers and investigators within the field to help in their efforts to apprehend criminals and suppress criminal activity. In this project, Kmeans Clustering is employed for crime data analysis.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python

Numpy , Pandas , Scipy
matplotlib
scikit-learn
seaborn

Database

SQLite

Read Before Purchase  :

  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.
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Movie Recommendation System Project Using Collaborative Filtering, Python Django, Machine Learning

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Latest Machine Learning Project with Source Code

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( Note : Project Included with Complete Source code Database Plus Documentation, Synopsis, Report)

Recommender systems are one of the most successful and widespread application of machine learning technologies in business. You can find large scale recommender systems in retail, video on demand, or music streaming.

A Web Base user-item Movie Recommendation Engine using Collaborative Filtering By matrix factorizations algorithm and thus the advice supported the underlying concept is that if two persons both liked certian common movies,then the films that one person has liked that the opposite person has not yet watched are often recommended to him.

A recommender system is a type of information recommend movies to user according to their area of interest. Our recommender system provide personalized information by learning the user‟s interests from previous interactions with that user[2]. In pattern recognition, the knearest neighbours algorithm (k-NN) is a flexible method used for classification. In following cases, the input consists of the k closest examples in given space. If k = 1, then the object is simply assigned to the class of that single nearest neighbour.

Algorithms Implemented 

  • Content based filtering
  • Collaborative Filtering
    • Memory based collaborative filtering
      • User-Item Filtering
      • Item-Item Filtering
    • Model based collaborative filtering
      • Single Value Decomposition(SVD)
      • SVD++
  • Hybrid Model
    • Content Based + SVD

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python3

Numpy , Pandas , Scipy

Database

SQLite

Requirements
python 3.6

pip3

virtualenv

Read Before Purchase  :

  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.
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Online Assignment Submission Project on Python Django

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Online Assignment Submission Project on Python Django is a system that enable the student to submit their assignment or project online without submitting any physical file. The proposed system helps reducing and minimizing human error, capable to assist supervisors in process controlling and managing students. During lockdown its very help full project.

Functional requirements to explain

identity:administrator, teacher, teaching assistant, student (each person has their own school ID number, within 10 digits, there can be letters, and everyone has a password)

Administrator: issue questions, delete questions 、Teacher information management, class organization (a teaching class has a main teacher, there can be no teaching assistant, or more than one teaching assistant)

Teachers: teaching assistant management (specify the teaching assistant's authority), class management, problem setting, assignment assignment, corrective assignment, statistics job completion

Assistants: rights specified by the teacher to complete all or part of the work, classroom management, the question, assignments, correcting homework, work statistics completion

Students: complete and submit the Assignment.

 

Technology Used in the project Online Assignment Submission 

  • 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 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

Student Login Details :

username: student@2
password: visg123456

teacher  Login Details :

username: teacher@2
password: visg123456

Admin  Login Details :

Link - http://127.0.0.1:8000/admin
username: visg
password: visg123456
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Django Project on Medical Shop Management System

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Latest Python Projects with source code

Medical Store Database Management System using Django

The main objective of the Django Project on Medical Shop Management System is to manage the details of Sells, Medicines, Stocks, Company,Inventory. It manages all the information about Sells, Medical Shop, Inventory, Sells. The project is totally built at administrative end and thus only the administrator is guaranteed the access. The purpose of the project is to build an application program to reduce the manual work for managing the Sells, Medicines, Medical Shop, Stocks. It tracks all the details about the Stocks, Company, Inventory.

Functionalities Provided By Django Project On Medical Shop Management System Are As Follows:

Dealer Management.

  • Add Dealer.
  • View Dealers.

Medicine  Management.

  • Add Medicine.
  • View Medicine.

Employees Management.

  • Add Employees.
  • View Employees.

Customers Management.

  • Add Customers.
  • View Customers

Purchase  Management.

  • New Purchase .
  • View All Purchase

Download Source Code

Installation Steps Django Project On Medical Shop Management System

Setting up the project:

Download the project zip file. Extract it.

Install Python3 in your system. Download the latest version. https://www.python.org/downloads/

Install django in your system using the following command:

pip install Django==1.11.6

Current version is Django 2.0.9 but this project uses the older version.

You can make use of any text editor such as Sublime, Atom, Pycharm, Webstorm etc. The link for Pycharm is mentioned below: Download the community(free) version. https://www.jetbrains.com/pycharm/download/#section=windows

Open Pycharm, open the extracted project folder in Pycharm. Go to Pycharm terminal and enter the following command:

python manage.py runserver

URL routing is handled in the file: pharma/urls.py

All the functionalities such as Create, update, delete, retrieve are present in the file: pharma/views.py

The database by default used with Django is SQLite3:

The database models are created in the file: pharma/models.py

Iff any changes are made in the models.py file such as adding, deleting fields or new tables, run the following two commands:

python manage.py makemigrations pharma python manage.py migrate

All the SQL queries are generated by Django implicitly. You can view the SQL commands using the following command:

python manage.py sqlmigrate pharma migration_name

"migration_name" is the file name generated during each Model file update, choose any filename from the folder and enter in the command to see the SQL commands of that update.