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

 

Online Job portal Project in Python Django with source code

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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

 

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Courier Management System Project in PHP

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Courier Management system Project in PHP  . In this project, you can simply perform the  operations to manage the couriers and the parcels. . You can log in as the system admin and also can add and delete the Courier. you can also Update Status for courier delivered or not .

Courier Management System is the simplest solution for Courier & Cargo Tracking Business.
This Courier Management System project will have different modules.
The login section will have login facility for the admin who will operate this system and online tracking system of consignment and shipping detail for domestic shipping.
While taking orders from its customers, it will take all the details of its customers who is placing the orders and all the details for the recipient such as its address,name,mobile number.

During billing process, system generates a consignment number for their products. Through this consignment no. customers or its recipient will able to track their products from any location using internet.

It will provide status of the product after placing orders.

This Courier Management System project will provide information recipient with following detail – where the current consignment is,till when it will reached its final destination, date of placing consignment , final date to reach its destination etc

Actors /Users of the System

  1. Admin
  2. Customers

ADMIN

  1. Login
  2. Admin can manage & update whole data
  3. Manage Shipment
    1. Add Shipper info, Receiver info and Shipment info.
    2. Edit/Update Shipment
    3. List all Shipment
    4. Search By Consignment Number
  4. Reports of the project
    1. Report of all customer
    2. Report of all consignment
    3. Report of all shipper
    4. Report of all pickup Date/Time
    5. Report of all status

Customers(Users)

  1. With Limited Access
  2. Users can check status of their product after placing orders.

Brief overview of the technology:

Front end: HTML, CSS, JavaScript

  1. HTML: HTML is used to create and save web document. E.g. Notepad/Notepad++
  2. CSS : (Cascading Style Sheets) Create attractive Layout
  3. Bootstrap : responsive design mobile freindly site
  4. JavaScript: it is a programming language, commonly use with web browsers.

Back end: PHP, MySQL

  1. PHP: Hypertext Preprocessor (PHP) is a technology that allows software developers to create dynamically generated web pages, in HTML, XML, or other document types, as per client request. PHP is open source software.
  2. MySQL: MySql is a database, widely used for accessing querying, updating, and managing data in databases.

Software Requirement(any one) :

  1. WAMP Server
  2. XAMPP Server
  3. MAMP Server
  4. LAMP Server

Installation Steps

1. Download zip file and Unzip file on your local server.
2. Put this file inside “c:/xampp/htdocs/” .
3. Database Configuration
Open phpmyadmin
Create Database named courier_db.
Import database courier_db.sql from downloaded folder(inside database)
4. Open Your browser put inside “http://localhost/Projectworlds Courier Management Sytem in PHP Mysql/“

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
  2. Numpy, pandas
  3. sklearn
  4. spacy
  5. Django 2.1
  6. pickle
  7. tqdm
  8. running the app:

Installation Step :- 

  1. Go to folder containing manage.py and run command: python manage.py runserver
  2. Once the server starts, open browser. The app runs on http://127.0.0.1:8000/
  3. fake_reviews.txt and real_reviews.txt contains some reviews that can be used to test the working of model.

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

 

 

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.

 

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.

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

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

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.

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

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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.

Project Features :-

  1. User can register and login.
  2. User can search through various movies and look through its details.
  3. User can give rating to the movies.
  4. User can add movie to their watch list.
  5. User can get movie recommendation (Recommendation algorithm (Collaborative Filtering) which suggests new movies based on the ratings given by user.)

Algorithm :

Collabortive Filtering (Recommender Algorithm)

  1. Collaborative filtering filters information by using the interactions and data collected by the system from other users. It's based on the idea that people who agreed in their evaluation of certain items are likely to agree again in the future.

  2. When we want to find a new movie to watch we'll often ask our friends for recommendations. Naturally, we have greater trust in the recommendations from friends who share tastes similar to our own.

  3. Collaborative-filtering systems focus on the relationship between users and items. The similarity of items is determined by the similarity of the ratings of those items by the users who have rated both items.

  4. There are two types of collaborative filtering

    1. User-based, which measures the similarity between target users and other users.
    2. Item-based, which measures the similarity between the items that target users rate or interact with and other items.

    I have used user based collaborative filtering in this project.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python3

Numpy , Pandas , Scipy

Database

SQLite

Requirements
python 3.7

pip3

virtualenv

Installation

pip install -r requirements.txt --user

Run server locally

$ python manage.py runserver

Go to localhost:8000

  1. Admin email - admin@admin.com
  2. admin pass - admin

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.