College Placement Management System Java Full Stack Project

Developing a Placement Management System Java Full Stack Project site for recruiting eligible candidates who have already enrolled their names in the Placement Office in an engineering college and a completely interactive site for the students to enhance their technical and communication skills and to be easily placed in companies.

Modules involved in this Full Stack Project are below:

1. Creating GUI Interfaces for the Recruiter, Student, and Admin and handling the inner logic of those three actors of the system.

2. Generating Reports of Online marks, eligible students for writing online tests, selected students for an interview, and final selected students. Sending emails to selected students and uploading files.

3. Online Test module

4. Providing practice sets for students and outsiders who visit the website

5. Creating a  Discussion Forum for handling student queries and other information which helps students in preparing for placements.

6. Send an SMS to the final selected students.

Download the Complete Java Full Stack Project on Placement Management System Source Code.

Fake Disaster Tweet Detection Web-App Python Machine Learning Project

This project “Fake Disaster Tweet Detection” aims to help predict, whether a tweet weather it is fake or real. It uses the Multinomial Naïve Bayes approach for detecting fake or real tweets from existing datasets available on Kaggle. The classifier will be trained only on text data. Traditionally text analysis is performed using Natural Language Processing also known as NLP. Natural language processing is a field that comes under Artificial Intelligence. Its main focus is on letting computers understand human language and process it. NLP helps recognize and predict diseases using speech, it helps in sentiment analysis, cognitive assistant, spam detection, the healthcare industry, etc. In this project Training Data is pre-processed, then sent to the classifier, then and the classifier predicts weather the tweet is real or fake.

This project is made on Jupyter Notebook which is a part of Anaconda Navigator. This project ran successfully on Jupyter Notebook. The dataset was successfully loaded into the notebook. All the extra python packages which were required for project completion were also loaded into the notebook. The model is also deployed successfully using HTML, CSS, python, and flask.

The accuracy score on test data is 77.977%. average recall value is 0.775 and the average precision score is 0.775. Precision is used to calculate a number of correct positive predictions made by the model. The recall is used to calculate the number of correct positive predictions made out of all the positive predictions that could have been made.

System Design

System Flowchart

System Flowchart

Problem: To detect disaster tweets whether it’s fake or real using a machine learning algorithm. In this, the concept of Natural language Processing is used.

Identification of data: In this project, I have used a dataset available on Kaggle competition based on Natural language processing. This project works only on text data. It has five columns:

  1. Id: It tells the unique identification of each tweet
  2. Text: It tells the tweet in text form
  3. Location: It tells the place from where the tweet was sent and it can be blank
  4. Keyword: It tells a particular word in the tweet and it can be blank
  5. Target: It tells the actual value of the tweet weather it’s a real tweet or Fake

Data-preprocessing: First the preprocessing is done in the dataset which includes the removal of punctuations, then the removal of URLs, digits, non-alphabets, and contractions, then tokenization and removing Stopwords, and removing Unicode. Then lemmatization is done on the dataset. After preprocessing Countvectorizer is used to convert text data into numerical data as the classifier only works for numerical data. The dataset is then split into 70% training data and 30% test data.

Definition of Training Data: The training dataset which contains 70% of the whole dataset is used for training the model.

Algorithm Section: In this project Multinomial Naïve Bayes classifier algorithm is used for detecting disaster tweets whether they are fake or real.

Evaluation with test set: Several text samples are passed through the model to check whether the classification algorithm gives the correct result or not.

Prediction Model

Implementation Work Details

The data-set which is used in this project “Fake disaster tweet detection” is taken from the Kaggle competition “Natural Language Processing with Disaster Tweets”. The data set contains 7613 samples. This project works only on text data. It has five columns:

  • Id: It tells the unique identification of each tweet
  • Text: It tells the tweet in text form
  • Location: It tells the place from where the tweet was sent and it can be blank
  • Keyword: It tells a particular word in the tweet and it can be blank
  • Target: It tells the actual value of the tweet weather it’s a real tweet

Step 2: Data-Preprocessing

  1. Removing Punctuations: Punctuations are removed with the help of the following python code
  1. Removing URLs, digits, non-alphabets, _: True means it has HTTP, and False means it does not have HTTP
  1. Removing Contraction: It expands the words which are written in short form like can’t is expanded into cannot, I’ll is expanded into I will, etc.
  1. Lowercase the text, tokenize them, and remove Stopwords: Tokenizing means splitting the text into a list of tokens. Stopwords are the words in the text which does not provide additional meaning to the text.
  1. Lemmatizing: It converts any word into its root form like running, ran into a run.
  1. Countvectorizer:

Text cannot be used to train our model, it has to be converted into numbers that our computer can understand, so far in this project, Countvectorizer is used. Countvectorizer counts the number of times each word appears in a document. Countvectorizer works as:

Step1: It first identifies unique words in the complete dataset.

Step 2: then it will create an array of zeros for each sample of the same length as above Step 3: It then takes each word at a time and find its occurrence in each sample in the dataset. The number of times the word appears in the sample will replace the zero positioned at the word in the list. This will repeat for every word. 

Step 3: Model Used:

In this project, the Multinomial Naïve Bayes approach is used for detecting fake or real tweets from existing datasets available on Kaggle. Naïve Bayes classifier is based on the probability theorem “Bayes Theorem” and also has an assumption of conditional independence among every pair.

System Testing

This project is made on Jupyter Notebook which is a part of Anaconda Navigator. This project ran successfully on Jupyter Notebook. The dataset was successfully loaded into the notebook. All the extra python packages which were required for project completion were also loaded into the notebook. The model is also deployed successfully using HTML, CSS, python, and flask.

The machine learning model is evaluated we normally use classification accuracy which is the number of correct predictions divided by the total number of predictions.

This accuracy measuring technique works well when there is an equal number of samples in the dataset belonging to each class. The accuracy score on test data is 77.977%. average recall value is 0.775 and the average precision score is 0.775. Precision is used to calculate a number of correct positive predictions made by the model. The recall is used to calculate the number of correct positive predictions made out of all the positive predictions that could have been made.

  • Precision = True Positives / (True Positives + False Positives)
  • Recall = True Positives / (True Positives + False Negatives)


In this project only one classification algorithm is used which is Multinomial Naïve Bayes. First, the preprocessing is done in the dataset which includes the removal of punctuations, then removal of URLs, digits, non-alphabets, and contractions, then tokenization and removing Stopwords, and removing Unicode. Then lemmatization is done on the dataset. After preprocessing Countvectorizer is used to convert text data into numerical data as the classifier only works for numerical data. The dataset is then split into 70% training data and 30% test data. The accuracy score on test data is 77.977%. average recall value is 0.775 and the average f1 score is 0.775.

Future Scope

In the future, some other classification algorithms can also be tried on this dataset like KNN, Support vector machine (SVM), Logistic Regression, and even Deep learning algorithms can also be used which give very high accuracy. Vectorizing can be done using other methods like word2vec, Tf-Idf vectorizer, etc.

Download the Complete Project on ake Disaster Tweet Detection Web Application Python-based Machine Learning Project.

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


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.


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.

Development of E-Commerce Store Portal using Bootstrap and ReactJS

The main aim of this project is to design, develop, and implement of E-Commerce Store Portal website based on HTML, CSS, Bootstrap, JavaScript, and ReactJS. To give a high openness of administration we will structure the online site that supports local businesses, with the goal that potential clients need not go to a physical shop to purchase items or administrations. The objective of this E-Commerce Store Portal project is to create an e-commerce web portal with a content management system that would allow product information to be updated securely using a system. The E-Commerce Store web portal will have an online interface in the form of an e-commerce website that will allow users to buy goods from the merchants. This web portal will allow local organizations and start-ups to start their businesses and reach out to the market.


This Bootstrap and ReactJS web portal project is aimed at developing an online static website for an E-commerce store that can be used by people to list their business on the website and will also provide customers to buy the products directly from the store. The website is based on HTML, CSS, Bootstrap, JavaScript, and React. Customers can buy the products directly from the store. This will help local businesses to register their products on the website. It will eventually help local businesses to increase their profits and people to buy the goods from the comfort of their homes. Users can see the products from the website. Our website will contain a Homepage where all the basic information about the store and details of the products will be available. This site is easy to operate and user-friendly.


The web portal will have an online interface in the form of an E-commerce website that will allow users to buy goods from the merchants. This web portal will allow local organizations and start-ups to start their businesses and reach out to the market


Online shopping practices are increasing rapidly, thanks to digitalization. Online E-commerce store owners are always eager to know how to increase traffic on their E-commerce sites and how to increase their profits to earn more revenue.  Hence by this, bringing together the various local businesses to a single platform so that anyone can access them anywhere according to their need. So, with the increasing importance of online sales and the growing number of customers visiting online stores we are going to develop a website that helps to save the time of the users. This website encourages local businesses to create their online store and supports them to run their business to grow.

E-Commerce Store Portal

Benefits of the proposed work

• Saves time for customers in quickly view various items on the E-Commerce portal.
• The ability to view and purchase items anytime, from anywhere with Internet access.
• Provides information about resort facilities.
• User-friendly interface.
• No Convenience fees.
• Total features of E-Commerce Website are accessible.
• Easy to use and simple to understand.
• Quick and save lots of time.

Modules and their functionalities:

1. Dashboard: This is the home page of our website.
2. Hawkers: This consist of all the hawkers available online for selling their products.
3. Feedback: The feedback area consists of all the feedback given by buyers.
4. Vendors: This contains information about the vendors and their products.
5. Place Order: This is used for placing an order of the product of your choice.
6. About Us: This page consists information of about the team behind the idea of promoting local businesses.

Implementation and User Interface

This E-Commerce Store Portal project is implemented with the help of Visual Code.

The user interface design was one of the core tasks in this project. The aim of UI design is to make the E-commerce application to be accepted and used easily.

Software Requirements:

  • Macintosh /Microsoft Windows /Linux
  • Virtual Studio Code or any other text editor
  • Chrome or any other browser

IoT based Attendance System Project Using Blockchain and JAVA MySQL

The success of this IoT-based Attendance System app will ensure that many more parents and organizations will be motivated to use this common platform. It becomes complicated when strength is more. With the increase in technology, attendance monitoring is designed with android or web-based applications. However, the intention of this design is to provide a Blockchain-based app that can be downloaded and used by the organization with no third-party control to meddle with the data.

There is an update option to modify attendance when it’s needed. However, the modifications are recorded and tracked, just in case, it’s a fraudulent activity. Attendance is captured using IOT automatically and is entered into the blockchain which makes the data tamper-proof, secure and robust. The privacy of its users is preserved because the user ids are generated by a trusted third party. This data is available for the government for Scholarships and other related decision-making.

IOT-based Attendance System using Blockchain is an application that is made for students and faculty of a particular college to maintain students’ attendance which is captured through an IOT device(biometric) and then the attendance is stored in the Blockchain. Blockchain is used in this application to ensure safety and a tamper-free environment as the data cannot be manipulated and is used for government purposes.


Generally, in many institutions attendance is monitored and marked using conventional systems like android or other similar web applications. Few conventional databases do not have features like checking whether any information has experienced unauthorized changes or not. In this system when the data is entered into the blockchain, no one is allowed to edit or delete the data.

This makes the application transparent and different from other web-based attendance systems as IoT is used to capture attendance through biometrics of the students in the class. Students’ poor attendance rate is one of the most challenging problems tackled by college management today. With the help of this application, student attendance rates can be improved which is also helpful for the government to take precise decisions regarding scholarship-like schemes for students with transparent data. Using blockchain and some encryption techniques, this application is made secure from any manipulations.

Student Login Page


The fingerprint module will collect fingerprint data from multiple users and sends it over the internet to the website. The IoT-based Attendance System website is coded in HTML, and CSS, JSP has a MySQL database, and records of attendance are stored in Blockchain. By logging into the website, the student can view all their attendance records. The timestamp of students’ attendance is encrypted and stored in the blockchain.


This IoT-based Attendance System application helps to automize the attendance system and makes it easy to manage all the data. Encryption, decryption, and blockchain make the application very secure. The application has a very user-friendly UI and is made to keep UI and UX in consideration.

The future enhancement of this IoT based Attendance System application is

  • To use Ethereum to make the application up to date with the technologies
  • To generate automatic weekly and monthly reports


Tables in the project.
Test case showing the home page after pasting the URL in the browser
Test case showing navbar functionalities working.
Test case showing login is done and navigated to the home page
Test case showing student registration is working.
Test case showing faculty registration
Test case showing faculty registration is working.
Test case showing attendance stored in blockchain

IoT based Attendance System Using Blockchain
Test case showing student’s attendance records.
Test case to get student report
Test case showing Student’s attendance report.
Test case showing download report is working
Test case showing all student details.
Test case showing all student’s attendance records.

Flow Chart Diagram:

Flow Chart

Architecture Diagram

Architecture Diagram

Usecase Diagram:

usecase diagram

Software Requirements

Programming Language: Java
Graphical User Interface: HTML, CSS with Bootstrap, JSP
Libraries: MYSQL connector jar file, Apache Tomcat jar file
Encryption Algorithm: SHA-256
Framework: Java EE
Tool: Eclipse, MYSQL

Hardware Requirements:

IOT Fingerprint Scanner

Resorts Management System Full Stack & Bootstrap Project


Resorts Management System Full Stack Website which is based on the user interface that is front end project which can be used by the customers to access the different types of rooms according to their needs and other facilities of the resort which include a banquet hall, restaurant service, rooftop pool service, etc. On our Resorts Management System website, we have included all the relevant details that user wants to access while searching for a resort, it has all valuable data or information.

Also if the user has any query regarding any service they can send us the message for the website. In our project, the Resort website that we have made is fully responsive which helps users to access it on any device or at anytime, anywhere. This application will help to improve services for tourists and also improve the revenue source for our resort.


In the present time, there is a great rush in Resorts, as these have become a necessity for everyone in the society. People travel a lot, stay in hotels and resorts, goes to the hotels for functions, meetings, and refreshments. Our Resorts Management System project is developed keeping in mind the general needs of the customers when he goes to the resort. We cannot deny that we are now in much more technological improvement and especially for business, shifting from a manual process to online.

It focuses on giving the customer all the information about the park and its activities. If a customer wants to come to the park, he can see the facilities available and know the park rates’ impact online. This will also save time for our customers as well as the administration with online booking instead of on-site booking. This Resorts Management System is very secure due to the availability of login and password options. Creating profits and achieving customer satisfaction is the main goal of our system.

Providing customer satisfaction is the main objective of our project. And we have also been taking care of the expectations of the users when they search for some resort for their holiday or any other event. It focuses on giving the customer all the information about the resort, and photos of the resort, and also help them to see the various room in the resort and book them in advance, various other pages are the About Us page, Facilities Page, Faqs page and Contact Us page, Blog Page. If any customer is willing to come to the resort, he/she can see the facilities available and can know the cost-effectiveness of the resort online.

This will also save time for our customers as well as management by booking online instead of booking on the spot. The resort system Full Stack & Bootstrap project’s main idea is to develop an online web-based application that is accessible to all and to create a scope for visiting tourists from different geographic locations. 


Technology Used:

  • HTML: The page layout will be designed in HTML
  • CSS: It is used for designing
  • JAVASCRIPT: To Program the behavior of web pages
  • Bootstrap: HTML, CSS, and JavaScript framework for creating responsive, mobile-friendly websites.

Resort Website


  • Microsoft Windows 7/8/10 or Linux
  • Vs Code or any other text editor
  • Chrome or any other browser


Teamwork plays a vital role to make any project successful. It needs the participation of every single member. Our team comprises four members and work will be equally divided among each member so that every member of the group can contribute and can give their best efforts on the Resorts Management System project.


Besides the above achievements, we still feel the project has some limitations, listed below:

  • Limited information provided by this system
  • Since it is an online Resort project, customers need an internet connection
  • People who are not familiar with computers or using online websites can’t use this software
  • Heavy traffic leads to failure or long wait issues


Online has got a clear advantage over the manual system. The Online Resort system is more reliable, efficient, and fast at the end of the project. I can say that online websites play a very crucial role in the development of the firm.

  • Thus we have proposed a Resort Fullstack Website.
  • It eliminates the 3rd party website completely ( MakeMyTrip, Goibibo, Trivago )
  • This software aims at reducing paperwork & provide multiple facilities to users with fewer efforts and Access to the Portal according to choice & availability.

We have prepared a full-fledged working website on named Resorts Management System. It is a website based on the Resort Management system and displays various facilities and services offered by the resort. We have displayed various features of our resort-like hotels, rooftop, spa, swimming pools, banquet halls, etc. The central objective of our Full Stack & Bootstrap Project website is to provide an online facility for accessing all the services of our resort.

We have created a platform where customers can directly communicate with us and can overcome the problems of manual system and third-party platform issues. This Full Stack & Bootstrap Project aims at reducing paperwork and provides multiple facilities to users with less effort. Users can access the portal according to choice and availability.

Web Technologies Project on Car Pooling Application

A brief walkthrough of the Car Pooling project

This Car Pooling application allows users to:

  • Become a member of the carpooling community (register and login)
  • Join rides
  • Offer rides
  • See the most popular rides taken

Joining A ride

The user searches for:

  • Source (starting point)
  • Destination (drop off point)

On selecting a ride, choose the number of seats (based on availability)

The cost of the ride will be displayed and will ask for verification of booking the ride

Offering the ride

A registered user creates a ride by

  • Selecting the starting point (source)
  • Selecting the endpoint (destination)
  • Entering car model (registration)
  • Enter the number of seats available
  • Cost per kilometer
  • The offered pickup points

 Inclusion of features

  • Webservices using RESTful APIs
  • Ajax Patterns
  • Submission throttling
  • To populate the source and destination of the list being searched
  • Multistage download
  • On loading the home page, the images are downloaded one after the other
  • Comet
  • SSE (server-sent events)
  • To view the topmost rides driven/ joined

Use of framework

  • RESTful API’s
  • Flask micro framework
  • Bootstrap for CSS
  • Mongo DB (for the database)

Intelligent Component

  • Calculate the fare: – ((distance * cost/km) / seats), Distance is calculated using the haversine algorithm
  • haversine algorithm – Takes two points (their latitude and longitude) and used to calculate the distance
  • To select pickup points – K nearest neighbors used
  • The intelligent component is trained using the dataset having rows as Place name, latitude, longitude
  • The model generated is used to predict the nearest neighbors of any given place
  • To decrease calculation time a pre-computed matrix of given places with respect to  distance  from all other points (places) is used

Two such matrices are used:

  • Distance matrix
  • Indexes matrix

Online Grocery Management Store PHP & MySQL Database Project

The main objective of this Online Grocery Management Web application is to provide an online product/ grocery purchasing website. Everyone needs food to survive. If someone wants to cook food even by themselves, they’ll first have to go to some grocery store, buy items, and carry all that heavy load of raw materials themselves to their home. That’s where an online solution can help so we have implemented the Online Grocery Store web application. All one needs to do is order everything they need for their cooking requirements online and relax till it gets delivered to their homes.

Online Grocery Store Challenges:

  • To keep a record of existing users, allow for the addition of new users, and removal of existing ones, and maintain several of their addresses
  • To maintain a catalog of available products, and categorize products into different types, and manufacturers
  • To maintain a cart for every user (one per user), and also all the orders he has done in the past, each order having multiple products, alongside the quantity they are available in.

Features of the Online Grocery Store project, and our plan to overcome the challenges mentioned above:

  • We will create tables for the user, address, product, category, manufacturer, order, cart, and product_order. E-R analysis is submitted along with this description.
  • The above model will be modeled in the form of tables and stored in SQL-based RDBMS, preferably MariaDB.

Wish List (features to be included if time permits)

  • To implement a user interface for addition, updation of products, manufacturer, etc.
  • To provide discounts for bulk orders, sales, etc.

Tables for Database Design:


  • User_Id
  • Email_Id


  • First_Name
  • Last_Name
  • Mobile_no


  • Address_id
  • Address_1
  • Address_2
  • Zip_Code
  • City
  • State
  • User_Id


  • Product_id
  • Product_Name
  • Units
  • Picture
  • Weight
  • Category_id
  • Price
  • Product_Description
  • Manufacturer_id


  • Category­_id
  • Category_description
  • Category_Name


  • Manufacturer_id
  • Manufacturer_Name


  • Order_id
  • Payment_Method
  • Order_time
  • Billing_id
  • Amount
  • Shipping_id
  • Address_id
  • User_id

Cart (User_Product)

  • User_id
  • Product_id
  • Quantity


  • Product_id
  • Order_id
  • Quantity
  • Price (of 1 unit)

ER Design Diagrams:

ER Diagram for Grocery Store Project

Functional Dependencies:

1) User Table
2) Address Table
3) Product Table
4) Category Table
5) Manufacturer Table
6) Grocery Order Table
7) Cart Table
8) Product Order Table

Other similar Projects on the amazon Ekart System:

We intend to create an amazon Ekart System with features also whilst incorporating all the amazing features that could be seen on amazon’s official website and more.


  • Homepage: The Landing/Homepage offers various navigating features including shopping, menu, finding products, add-to-cart payment, etc., as shown in the image above.
  • Shopping: schedule your delivery with UPI pay.
  • Rewards: provide a program membership offer by incorporating sign-in features and provide free delivery on some products.
  • Careers: facility to explore career paths by imparting internships and apprenticeships and jobs by logging in/registering on the portal and providing too many jobs in many different ways.
  • Payment and cashback offer to make payment via this app – amazon pay.
  • Delivery: Accepting the orders and delivering them to the user’s doorstep.


  • Login page.
  • listing page
  • Profile page
  • Prime page
  • Sign in with the prime page
  • Payment with prime page
  • Payment page
  • Order page

Technologies To Be Used:

1. Front-End:

  • HTML5
  • CSS3
  • Bootstrap
  • javaScript
  • Reactjs

2. Back-End:

  • Nodejs
  • Expressjs

3. Database

  • MongoDB

Pharmacy Management System Python Bootstrap Project


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.


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.


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.


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

Alumni Management Portal for Educational Institutions PHP Project

This Alumni Management Portal project aims to develop a platform for the Educational Institutions to maintain communication between faculty, alumni, and students. The main objective of this PHP & MySQL web application is to enhance or improve employability opportunities for students, organize alumni lectures and events, and increase networking skills and opportunities for students.


Our Institutional Alumni Management Portal project is a web-enabled application through which administrators, students, and alumni will be able to continuously communicate. For this, we need an application that is user-friendly. The needs of all three users of the portal should be covered.

Administrators can log in, maintain, verify and manage alumni and student records, and create events and group chats. Administrators can also generate reports in excel and pdf format and view placement statistics of the institute. Once an event is created, all the alumni and students registered on the portal will receive emails automatically.

Alumni can log in, register, update their details, view events, participate in group chats, and post job opportunities and materials. Students can log in, register, view details of alumni, register for events, download materials posted by alumni, job materials posted by alumni, and participate in group chats with other alumni and students.

The below figure shows and explains the Architecture Diagram of the Alumni Management Portal website.

Architecture Diagram of Alumni Management Portal


To build a responsive Educational Institutions Alumni web application to manage and track students and alumni of an educational institute and build a portal that facilitates continuous communication.

Organization of Project

We have three modules in our project.

● Admin
● Alumni
● Student

Alumni management portals can be used by educational institutes to maintain and manage records of alumni and students of the respective institute in an efficient manner. It leads to better outcomes in terms of student placement opportunities, and knowledge transfer and improves the structure of communication between current students and alumni thereby increasing networking skills and opportunities.

In traditional methods where student data is manually stored, a lot of time is wasted doing redundant and repetitive work. Here storing data, statistics, and report generation all are automated. Furthermore, there is no need for paperwork since everything is done online and stored in a refined database.

The output screenshot of the project explains about “Test case check whether the admin is able to view all users”

All Users List of Alumni Management Portal Project