Category: Python projects

Welcome to the Python Projects Section.

Find the below some projects we have developed using Python.

  1. Google Finance currency calculator API.
  2. using aprori on website to get relevent data
  3. Game like tictactoe,2048,
  4. Note/content sharing platform for college students
  5. book information parsing tool developed for opencart
  6. AI Crooswords
  7. An algorithm for steganography of multiple heterogeneous files on a single video carrier.
  8. college-administration
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Intelligent Customer Help Desk Python and Node-Red Project

Project Summary:

In this Intelligent Customer Help Desk project, we need to create a chatbot application that can answer the question(s) that falls outside the scope of the pre-determined question set.

This can be done using a chatbot that will use the intelligent document understanding feature of Watson Discovery. 

Project Requirements:

IBM Cloud, IBM Watson, Python, Node-Red.

Project Scope:

In this Python and Node-Red Project, we need to create a website first using HTML code. Next, we should create a chatbot with help of IBM Watson Assistant and Watson discovery.

Using Node-Red we need to build a web application that integrates all services and deploys the same on the IBM cloud.

This project will answer all queries of the user and if any question falls outside the scope of the predetermined question set then this project will use the Smart Document Understanding feature of Watson Discovery to train it on what text in the owner’s manual is important and what is not.

This will improve the answers returned from the queries.

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Class Scheduling System Python Project using Django Framework

Present issues:

  • No digital class management system
  • Fixed timetable which cannot be changed throughout the semester
  • Cannot swap classes easily
  • No publishing mechanism
  • No administrator
  • Students cannot access the present-day schedule

Proposed solution:

  • Dynamic mechanism to change weekly class schedules
  • Publish new schedule after changes
  • Fully manageable through administrator privileges
  • Secured using username and password credentials
  • Schedule accessible on the internet
  • Administrators can access the portal onsite only
  • The system can be implemented in other departments and also

Architecture

  • Any machine can connect to the server
  • Administrators can access only the campus network
  • Students and faculty can access it as long as there is internet
  • Server-side will manage access and manipulation rights
  • The server will also publish a current schedule

Technologies

Django Framework:

  • Manages all 3 tiers(MVT – Model, View, Template) to run the web application.
  • Front-tier or client employs HTML and CSS via Django templates.
  • The Server-side uses Python to implement the logic for managing model-based objects.
  • The Server-side also enforces security standards.
  • The back end contains an in-built database, accessible via a web address generated by a virtual machine managed by the Django framework.
  • Can deploy web application after completion of web-application construction.

Use Case Diagram

Class Scheduling System
Interface Diagram:

Interface Diagram

Output Screenshot:

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LSTM based Automated Essay Scoring System Python Project using HTML, CSS, and Bootstrap

Introduction

Essays are a widely used tool to assess the capabilities of a candidate for a job or an educational institution. Writing an essay given a prompt requires comprehension of a given prompt, followed by analysis or argumentation of viewpoints expressed in the prompt, depending on the needs of the testing authority. They give a deep insight into the reasoning abilities and thought processes of the author, and hence are an integral part of standardized tests like the SAT, TOEFL, and GMAT.

With essays comes the need for personnel qualified enough to carry out the process of grading the essays appropriately and ranking them on the basis of various testing criteria. Our project aims to automate this process of grading the essays with the aid of Deep learning, in particular, using Long Short Term Memory networks which is a special kind of RNN.

Automated Essay Scoring (AES) allows the instructor to assign scores easily to the participants with a pre-trained deep learning model. This model is trained in such a way that the scores assigned are in agreement with the previous scoring patterns of the instructor. So this needs the dataset which contains the information of scores given by the instructor previously. AES uses Natural Language processing, a branch of artificial intelligence enabling the trained model to understand and interpret human language, to assess essays written in human language.

Problem Definition

Given the growing number of candidates applying for standardized tests every year, finding a proportionate number of personnel to grade the essay component of these tests is an arduous task. This personnel must be skilled and capable of analyzing essays, scoring them according to the requirements of the institution, and be able to discern between the good and the excellent.

In addition to this, there are a lot of time constraints in grading multiple essays. This can prove to be cumbersome for a limited number of human essay graders. Having to grade several essays within a deadline can compromise the quality of grading done. Thus, there is a clear need to automate this process so that the institution carrying out the grading can focus on evaluating other aspects of the candidate’s profile.

The challenge was to create a web application to take in the essay and predict a score. We need to train a neural network model to predict the score of the essay in accordance with the rater. The model is to be made using LSTM.

Approach

In order to meet the need for automation of essay grading, we propose an application that provides an interface for users to choose an essay prompt of their choice and provide a response for the same. The user’s response is graded by the application within seconds and a score is displayed.

This application makes use of the technologies of Natural Language Processing that performs operations on textual input, and LSTM, which is used to train a model on how to grade essays. The application also uses the Word2Vec embedding technique to convert the essay into a vector so that the model can be trained addresses the issue of time constraints; automated grading takes place within seconds as compared to physical grading which requires minutes per essay. The net amount of time saved over a period of consistently using the application is vast; costs of maintaining human graders are also saved.

The application gives an output from the pre-trained LSTM model. The model is trained using a dataset provided by Hewlett Foundation in 2012 for a competition on Kaggle.

Web Application (Output)

The front end of the application was implemented using HTML, CSS, and Bootstrap. It provides the option for users to choose from a set of prompts and write an essay accordingly or to grade their own custom essay.

The landing page of the application:

Automated Essay Scoring System

Software Specifications

This application is developed primarily using Python, for the purposes of running the app. The model was built and trained on Jupyter Notebook. The front end of the application was designed with HTML, CSS, and Bootstrap. All the components of this application were integrated with the help of the Flask App, and the final project was deployed on IBM Cloud.

While training the model, the dataset was imported into the model with the Pandas library. Pandas library used was v1.3.0. Numpy v1.19.2 was used to handle array data structure. Natural Language ToolKit v3.6.2 was used to tokenize essays to sentences written in English and also to remove stopwords to make sure the sentences contain only relevant words. RegEx(re) package v2.2.1 was used to remove unnecessary punctuations and symbols present in the essay or sentences. Our model utilizes the Word2Vec technique to convert words to corresponding vectors. Word2Vec v0.11.1 was used to convert words into vectors. Tensorflow v2.5.0 was used to build the model. ScikitLearn v0.24.2 was used for data preprocessing.

To make use of the application, the user needs to have access to a stable internet connection and an operating system compatible with the latest versions of most browsers. In the absence of an internet connection, the application can be run locally. Still, the user needs to have the authorization to access the source code of our project for the same, which is not recommended for intellectual property purposes.

Future Scope

This application could be integrated and used by several testing institutions to meet their needs for essay grading. The model used could be trained with an increasing number of input essays to further improve its accuracy. The model could also be trained on giving a score on specific criteria of essay grading such as relevancy, linguistic and reasoning ability of the author. Research could be conducted on making the model faster. This technology could also be extended for use with languages other than the English language, effectively rendering it useful on a worldwide level.

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Intelligent Access Control for Safety Critical Areas Project using IoT Analytics and IBM Cloud Services

Purpose of the Project

  • Access control is done by using a smart Analytic device. It verifies the entry of the person.
  • The Smart device verifies the persons entering into the industry.
  • The details of the person are being taken and uploaded into the cloud.
  • We can Restrict the entry of unknown persons and we can restrict the persons who are not following the safety measures by using this IoT device.

Existing Problem

The Intelligent Access Control problem with the present existing device is it cannot able to identifies the safety measures of the persons it just identifies the entry of the persons.

Proposed Solution

We can make use of IoT Analytics in Access Control, such that during working hours in the industry we can identify the persons who are following the safety measures and who are not following.

 Also, with the usage of IoT, automatically, the details of the person are taken and we can restrict them.

Hardware/Software Designing

The Intelligent Access Control Software design involves general We used IBM Cloud Services to create the Internet of Things platform. In the IoT platform, we create a virtual Raspberry Pi device. After creating the design we get the device credentials. We use these credentials in the Python program then we integrated the Node-Red platform with IoT. With the help of MIT APP Inverter, we designed the app & integrated it with the Node-Red to observe the values.

Experiment Investigation

To complete our Intelligent Access Control project work we collected the required data from Google & research papers. After getting complete knowledge we work according to our roles in the project. At first, we create the IBM Cloud account then we created the Internet of Things Platform after we wrote a python code in IDLE to connect IBM IoT Platform. Next, we created the Node-Red Services. This service helps us to show virtual flow graphs. We connect Node-Red to IBM IoT to get the current, and voltage, and calculate bills. From Node-Red we send values to the MIT APP. From the app, we can view the details of the person.

FLOWCHART

Flow Chart

MIT APP:

MIT App

ADVANTAGES & DISADVANTAGES

Advantages:

1) Increase ease of access for employers

2) Keep track of who comes and goes

3) Protect against unwanted visitors

4) create a safe work Environment

5) Reduce Theft and Accidents

6) Easy Monitoring

Disadvantages:

1) Access control systems can be hacked.

 APPLICATIONS

1) Large Industries

2) In Airports

3) Government Sectors.

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E-Commerce Application Project using Python Django Framework

PROBLEM STATEMENT FOR E-COMMERCE WEBSITE

An E-Commerce Website selling a wide variety of products needs to be developed. Products must be grouped into categories based on their characteristics. Some of the broad categories include Electronics, Apparel, Books & Media.

For eg, mobile phones and laptops come under the category Electronics, and T-shirts and pants come under the category Apparel.

The webpage should provide a search bar for the user to search for the products of his/her choice and should provide functionality for an admin to log in and modify the database.

The backend of the website should comprise a database to store:

1. The list of products available
2. The various categories of products available
3. The list of sellers available
4. Table of details of all the users who have purchased items.

The specifications of the various items in the database are given below.

A PRODUCT has the following requirements

– Each Product has the following attributes to identify it Name, ID, Seller, Price, Colour, Number of Items Left
– Each product may have a number of SELLERS.
– Each Seller has a location, products he/she is selling, discount he/she is willing to offer on the products as well as the time of delivery.

The products are organized into CATEGORIES.

– Each Category has a name and an ID.
– Each Category may be further subdivided into more categories.

Eg: Electronics is a broad category that is comprised of a number of products such as Laptops, of which Dell Inspiron is a type of Laptop.

The database must store data of the various USERS of the website

– Each user has a name, address, price to be paid, and ID of the product purchased.

Admin logs in to the PRODUCT database to add new products, and delete and modify the existing database.

Physical Design

E-Commerce Project Computation of the Blocking Factor for each of the Tables with the use of the standard block size of 512 bytes. The Blocking Factor is a lower-limit integer value as part of the tuple cannot be saved in one block of data storage.

List of Entity Types

Goods – This table has details of all the Goods in the Database.

Seller – This table has the details of all the Sellers in the database.

Product – This table has the details of all products being sold.

Customer – This table has the details of all customers who have registered with the website.

Customer Items – This table has the shopping cart of all the customers.

Book – This table has the specifications of all books being sold.

Fashion – This table has the specifications of all fashion apparel being sold.

Media – This table has the specifications of all Media being sold.

Mobile – This table has the specifications of all Mobiles being sold.

TV – This table has the specifications of all TVs being sold.

Laptop – This table has the specifications of all Laptops being sold.

All Columns are NOT NULL unless explicitly mentioned

Relational Schema:

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Airbnb User Bookings Prediction Project Synopsis

Airbnb User Bookings Synopsis

1. Objective of work

The main objective of this project is to predict where will new guest book their first travel experience. 

2. Motivation

This project helps Airbnb to better predict their demand and take consequent informed decisions. Earlier a new user was overwhelmed with the various choices available for a perfect vacation or stay.

By predicting where a new user will book their first travel experience the company is better able to inform its users by sharing personalized content with their community. It will drastically decrease the time to first booking which will increase the company’s output and help them gain popularity among its user and an edge over its competitors in the market. 

3. Target Specifications if any

Predicting where a new guest books their first travel experience. 

4. Functional Partitioning of the project

4.1 Research and gaining knowledge

Undertaking various courses and familiarizing ourselves with the working process of Data Science problems. Exposure and exploration of the Kaggle website, understanding kernels, and datasets. Learning the prerequisites: programming in Python, and Pandas along with Machine Learning algorithms and data visualization methods.

4.2 Frequent Discussions and Guidance

Frequent discussions with our mentor along with his guidance in the same will allow us to work in the right direction and take informed decisions.

 4.3 Applying the knowledge gained

After much exposure to this field and gaining the knowledge, we will now apply our skills to real-life problems and contribute to society.

5. Methodology

5.1 Using the Kaggle platform

In the test set, we will predict all the new users with their first activities after 7/1/2014.In the sessions dataset, the data only dates back to 1/1/2014, while the user’s dataset dates back to 2010. Taking the help of the Kaggle platform for testing out datasets as it is not feasible to have a large dataset say 1TB be stored in a local machine.

5.2 Working on the dataset

 Using the dataset and studying various patterns of users’ first booking after signing up with Airbnb from different countries. Next plot out the observed and collected information. We can then apply various Machine Learning algorithms and calculate prediction scores. Finally, choose the algorithm with the highest score to recommend to users which are from that country the destinations that have been frequently used by travelers belonging to that region.

5.3 Submitting our work on the Kaggle platform

The result can now finally be uploaded on the platform and be used by Airbnb to better connect with their users.

6. Tools required

6.1 Kaggle Kernels

Kaggle is a platform for doing and sharing Data Science. Kaggle Kernels are essentially Jupyter notebooks in the browser that can be run right before your eyes, all free of charge. The processing power for the notebook comes from servers in the cloud, not our local machine allowing us to experience Data Science and Machine Learning without burning through the laptop’s battery and space.

6.2 Dataset

Airbnb will be providing us with the dataset, which would contain: Airbnb will be providing us with the dataset, which would contain

  • csv-the training set of users
  • csv-the test set of users
  • csv-web sessions log for users
  • csv-summary statistics of destination countries in this dataset and their locations
  • csv-summary statistics of users’ age group, gender, and country of destination.
  • csv-correct format for submitting our predictions

7. Work Schedule

(a) January

Enroll and start the course on Machine Learning using Kaggle. Start recapitulating the basics of Python and its various libraries such as NumPy, pandas, etc.

(b) February

End course and start analyzing the dataset

(c) March

Start coding and implementing various algorithms for the prediction

(d) April

Pick the final algorithm by trial and test and finish coding

(e) May

Appropriate documentation and upload our solution

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Online Medical Shop DBMS Python Mini Project

This project is based and innovated on an Online medical shop, wherein we store all the details about the customers, the stock of the medicines, orders, and payments and also the project will include a page wherein the user will indicate the symptoms and will get a probable disease and the prescribed medicine.

The project is aimed to modernize and support existing small business owners. In the age of technology where online medicine is dominated by e-commerce giants such as 1mg, net meds, etc. We wanted to develop a solution for small business owners as well.

The existing Medicinal systems have the provision for any user to book a request for a particular medicine through e-commerce. And further, the traditional methods to visit the medicinal centers for mere inquiry are time-consuming and monotonous and the non-availability is disappointing.

The data relevant to the processing of the request may or may not be manually stored or be captivated in a file system that is prone to manual errors, inconsistency, redundancy, and difficulty in retrieval. With our system, the availability can be shown so, even if a customer wants a pickup of his/her medicine. they can do so without any problems. Our model also has an integrated web scraper, which is an innovation we have come up with. This scraper can scrape medicines off the net for data warehousing.

This system maintains the storage details of all the customers and medicines that are stored in the shop. The system will keep track of the orders made and the payment details. NoSQL will be used to store future suggestions and customer reviews.

The main part of the project will be a part where the customer will be able to select his/her symptoms and medicine will be referred to them. Along with the expected disease. We also would integrate Web Scraping of all the medicines related to a particular disease entered by the user to store it in our database.

Software Requirements

• Language support required: Python 3.5 or later, HTML5, JS, CSS3
• NoSQL database required: MongoDB
• Relational Database required: MySQL
• Windows 7 or 10 /Mac OS X 10.11 or higher, 64-bit /Linux: RHEL 6/7, 64-bit (almost all libraries also work in Ubuntu)
• Heroku and pip are preferred for deployment and installation of packages (such as Django,asgerif, mongoose, etc) specified in requirements.txt
• A web browser support is needed.

If using the software through deployment, no language support in your machine is required.

Conclusion & Future Enhancement

This project was successfully built and completed. The project is an online medical shop with two categories of users (admin and customer) who can update inventory and place orders respectively. We have also added a web scraper as an innovation to this project. However, there are a lot of changes and addition of functionalities that can be done, which we intend to do after peer and faculty review.

Some of the enhancements are :

  • Listing of products linked with images to generate a more shop-relevant UI
  • Remove some programming language constraints
  • Online Deployment
  • And changes that our faculty and peers suggest.

In the end, we would once again thank our college, examiners, faculty guides, and teachers to help us finish the project within the speculated timeline.

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Development of Speech Recognition AI Project with Python

Methodology

Working on the Speech Recognition Python Project. Design and Development of Speech Recognition AI Project with Python Source code, report, and ppt using NLP, PLP, and Deep Neural Networks.

Speak– The assistant will speak the following introduction, the output, and the following things according to which good is given. It will use the laptop microphone to hear the input from the user and later recognize the voice said by the user and match the code words and if anything matches it will show the output.

Wish Me-The assistant will speak the Message included in the introduction even if it will wish the morning afternoon and even the evening depending upon the real-time based scenario. It will wish the morning from 04HH to 11HH 59MM. It will wish the afternoon from 12HH to 17HH 59MM. It will wish the evening from 18HH to 03HH 59MM.

Take Command– The assistant will take microphone(speech) input from the user and returns string output. It will be sub-divide into many different parts as described below. Listening-The assistant will open the microphone and try to hear what the user wants to convey to it.

Recognizing– The assistant will try to recognize the input spoken by the user and then check the code whether the word that is recognized by the assistant is there or not if the input matches it will show the output otherwise it will speak “Say that again please” this line which means to give the input again by the user. If the word is correctly recognized, it will follow the instructions assigned to it.

Wikipedia– If the word is recognized as “Wikipedia” it will search Wikipedia according to the input given by the user. E.g. if we say Narendra Modi Wikipedia so the assistant will speak “searching Wikipedia Narendra Modi” and then after it “According to Wikipedia…” and the details of that particular person. Youtube- If the word is recognized as “YouTube”, it will open the internet explorer and directly start opening the default web browser by the link “youtube.com”.

Google– If the word is recognized as “Google”, it will open the internet explorer and directly start opening the Google by the link “google.com”.

Train Information– If the word is recognized as “Train info”. It will fetch the detail from a CSV file and returns the detail of all the train and display them on the terminal. Stack Overflow- If the word is recognized as “Stack Over Flow” it will open the internet explorer and directly start opening the Stack Over Flow website by the link “stackoverflow.com”.

Play Music– If the word is recognized as “Play Music” it will search the .mp3 or .mp4 file in the default path of the device that is provided by the programmer in the programming. E.g. if we say Play Music so the assistant will search in the path like “D:\\Non Critical\\songs\\Favourite Songs2” and it will play that particular song. The Time- If the word is recognized as “The Time” it will check the real-time from the device and speak the same in terms of “HH:MM: SS”. E.g. if we say the time so the assistant will check the time and if the time is 08:14:21 P.M. it will speak “Sir, the time is 20HH:14MM:21SS”.

Open Code– If the word is recognized as “Open Code” it will search the .java or .py file in the default path of the device that is provided by the programmer in the programming. E.g. if we say Open Code so the assistant will search in the path like “C:\\Users\\XYZ\\AppData\\Local\\Programs\\project.py” and it will open the code. Stop- If the word is recognized as “Stop” it will speak “Quitting sir thanks for your time” and the code terminates.

Code-Snippet

Speech Recognition Project Coding

Algorithms used in Speech Recognition

NLP (Natural Language Processing) & Tokenization
PLP
Deep Neural Networks
Discrimination training
WFST Frameworks etc;

The following must be installed-:

1. sudo pip install SpeechRecognition.
2. Sudo apt-get installs python-pyaudio python3-pyaudio or pip install pyaudio.
This is the most important module in your project as it provides the main functionality in our project to convert speech into text.

Future Scope

This specific area of AI ends up being productive in each specialized field. We have additionally actualized this to show how it is valuable in various fields as we have made a little undertaking to exhibit its use in various documented, for example, railroad, looking through feed and so on; Like PCs began to play chess better than human, speech recognition before long will be improved by PCs as well. Critically, that will include some significant information about nature in general and the human mind specifically. So speech recognition is a significant advance in our investigation of natural laws. Our venture can be utilized by railroads and another center point to show distinctive data utilizing speech recognition.

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Pharmacy Management System Python Bootstrap Project

Abstract

In a Pharmacy, usually, all the activities are carried out manually, but it is not suitable when we need to store large data. If we are using software, all the data related to inventory management, view, and modification of stocks, sales, and billing are permanently stored in a storage file.

This Pharmacy Management System project was developed mainly for pharmacists and it is easy to use and maintain, this software is also quick, reliable, and accurate.

Introduction

Pharmacy management system stores data and enables functionality that organizes and maintains the medication use process within pharmacies. These are independent technology for pharmacy use only. It is designed to improve accuracy and efficiency in pharmaceutical stores.

The main aim of the pharmacy management system is to assist pharmacists in the safe and effective delivery of pharmaceutical drugs. The pharmacists can maintain records related to stocks and sales through the pharmacy management system.

The user can control the buying and selling process, and view and manipulate the stocks. The user can also generate the bills after the transaction completes.

Objective

The platform provides the following features:

  • View and Update the stocks
  • Billing
  • Sales Report
  • Search Receipt
  • Overall Inventory Management

Scope of the Project

As far as an existing system has established an understanding of how useful a web platform is to use for a common man. However, efforts have to be made to make local Pharmacies digitize the business on the online platform.

The Scope of this project is to develop a Web Application using the concept of File Structures (Variable Length), which makes users run it on a simple browser that is user-friendly in the current era and it is very easy for the browser to send and receive data over the internet.

Problem Formulation and Proposed Solution

Problem Statement

In Pharmacy Management System all the data related to inventory, sales, stocks, and billings are kept in paper records, managing all these records is a difficult task. The time required to manage all these activities is considerably high. In order to overcome these problems, we can use Pharmacy Management System.

The role of Information Technology in Pharmacy practice is dynamic and not likely to lose relevance in the coming years. Pharmacists are interested in Information Technology because it increases efficiency in our daily tasks and improves the accessing of information stored.

Users of Pharmacy computer systems are generally limited to Pharmacy staff members, who are given usernames and passwords to access the system to ensure Data Protection.

This allows the employer to prevent unauthorized access to protected health information and keep a record of who performed each task in case an error occurs. Pharmacy staff should protect their usernames and passwords and avoid giving them out to unauthorized individuals. Backup and maintenance of pharmacy computer systems are essential to the continued function of the system.

Pharmacy Management System Python Project

Result and Discussion

Effective implementation of this software will take care of the basic requirements of the Pharmacy Management System because it is capable of providing easy and effective storage of information related to Pharmacy activities.

(a) Login page
(b) Menu
(a) Add
(b) Inventory
(a) Billing
(b) Receipt
(a) Sales Report
(b) Search Receipt

Conclusion and Future Work

In this section of the report, we finally conclude that using Pharmacy Management System is a very reliable, accurate, easy backup, and time-saving software.

Conclusion

In Conclusion, we would like to state that this Pharmacy Management System software enhances the Pharmacy work culture by eliminating the human-time consuming and tedious tasks, which can be done by this software.

This system has the ability to keep track of records of the product’s stocks and sales. The main purpose is effectively and easily handle pharmacy data and its management.

Future Work

In addition to the existing Pharmacy Management System project, we look forward to reaching many people by making it Open Source. It can be done by hosting this project on a cloud server like Heroku.

Since we are using a text document to store the data it can be placed in scalable object storage like Amazon S3 and then a Data Pipeline can be built between them.

Related Projects on Pharmacy Management System below:

Pharmacy Management System DBMS Project
Online Medical Store or Pharmacy Shop Java Project with code
Online Pharmacy Management System Java Project
Pharmacy Management System CSE Assignment Report
Pharmacy Management System Project in C#.Net
Pharmacy Management System Project in Java
VB project on Data management system for a Pharmacy shop
Development of Medical Store Management System Java Project
Online Healthcare System Python & SQLite Django Framework Web Application Project
Role of digital strategy in the globalization of Medium-scale Indian Pharmaceutical Industries
Patient Follow-up Java Project
Public Healthcare Management System Manual Testing

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Online College Complaints Suggestions and Compliances PHP Project

“Online Complaints, Suggestions, and Compliances” is a website and through this project, we have learned to design webpages using HTML, CSS, JavaScript, and PHP. It has enabled us to have a deeper understanding of how frameworks help in the development of websites.

Thus, in this College Complaints Suggestions and Compliances project, we have acquired a lot of knowledge about various technologies in web development. We have explored many new concepts on the web, such as JavaScript and AJAX.

Solving these problems must be quick and transparent. Often, Students shy away from writing complaints and suggestions into the box, as students are afraid of peer views of their actions.

Problem Statement:

A College Complaints Suggestions and Compliances Web Application to allow students to raise complaints and suggestions transparently without exposing their identity.

The Objectives of this application are:

  • Allows students to log in with E-mail verification before submission of complaints and suggestions
  • Allows students to track their submissions from their portal
  • Allows teachers to reply and change the status of the submissions without exposing student’s USN
  • Provides a pie chart representing the types of submissions
  • Admin can block submissions using foul languages

Steps Involved:

Step 1: The web application loads the home page. With options of Student, Teacher, and Admin
Step 2: On selecting Student, the user must first log in using the login page. On successful login, the user is greeted with the student portal.
Step 3: The student portal allows users to check the status of the previous complaints, and suggestions.
Step 4: Students can click to submit complaints and suggestions, after the verification page using email verification.
Step 5: The student can click on the logout option and is then taken to the login page, which has the option to return to the home page
Step 6: On clicking the teacher option on the home page, the teacher has to log in. They are then taken to the teacher’s home page. The teacher can choose to view complaints and suggestions.
Step 7: On clicking on a complaint/suggestion is taken to the auction page. Where the teacher can change the status of the complaint with the option to reply.
Step 8: After replying, the teacher is taken back to the teacher’s home portal. On logging out, you are taken to the login page, with the option to be redirected to the home page
Step 9: On clicking the Admin option on the home page, the admin has to log in. They are then taken to the Admin home page. Admin can choose to view complaints and suggestions which are blocked due to the use of foul language. After allowing or disallowing the admin is returned to the Admin home portal. On logging out, you are taken to the login page, with the option to be redirected to the home page.

Functionalities

Login: This module allows students, teachers, and Admin to log in with different access to web pages and controls. The username and hash of the password are compared with the Tables stored in the Database.

Signup: This module allows students, teacher, and Admin to register their access to the web application. By signing up the user is registered in the Database, so that their login credential is saved for future logins.

Complaint and Suggestion Form: This module allows students to fill up complaints or suggestions. Before uploading to Database. It checks for any foul language violation. It marks the entry to be checked by the admin, before allowing it to be viewed by the teacher.

Student Submission view: This module allows students to view the status and reply to their complaints or suggestions previously raised. On clicking the user can re-open a submission.

Verification: Students have to enter the One-time password (OTP) sent to their email to verify the authenticity of the complaint and suggestions. This module is implemented using PHPMailer Library.

Teacher submission view: This module allows concerned teachers to view the complaints and suggestions without revealing students’ USN. On selecting a submission, the teacher can update the status, reply to the same, and notify the same to the student’s email.

Admin unblock submission: Display submissions flagged due to misuse of the platform. Allows admin to unblock the submission if it is appropriate, and updates the same in Database.

Application

  • This application can be used in all colleges to collect Complaints and suggestions
  • It can be viewed by a teacher without relieving student’s use
  • Students get updated by their emails when the status of their submissions changes
  • The teacher can view a pie chart representing the type of submission. So, they can concentrate on a given area
  • Submissions using foul language are blocked and are reviewed by Admin

Conclusion

The internet is a very powerful platform for people to share their views in confidence. This Web Technology Mini Project draws further on the notion of the same lines, allowing Students to place complaints and suggestions without relieving their identity. Colleges and institutions improve from the useful insights provided through our platform. Thus, allowing them to understand the needs of students wholesomely.

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