Category: Academic Projects

Download Free Academic Projects for CSE, ECE, EEE, Mechanical, MBA Students. Here you can download the full project source codes, reports, ppts, synopsis, DFD, Flow Charts, UML Diagrams, Design Documents, etc.

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College Classroom Check and Fill Mini Project Synopsis

Introduction

The Classroom Check and Fill project is to prepare a website that tells the current status of a particular room. It tells whether a class is going on or empty or there is no class in that particular room. . It uses the technologies like PHP, python, java, MySQL, and many more. With the help of this website, a teacher or a student can know the status of the room and work accordingly.

Objectives

The aim of our project is to help teachers and students to check if in the room a class is going on if the room is empty or if there is no class in that particular room.

1. To help the HODs and teachers to check whether the venue is empty or not (in one click)
2. To help students to check their timetables with ease
3. Provides user-friendly application

Methodology/ Planning of work

Step 1: GATHERING RELEVANT INFORMATION
Our project is to help teachers and students to check if in the room a class is going on or the room is empty or there is no class in that particular room. We will take the relevant information from the CR of a particular class of IT and update the status of the room accordingly.

Step 2: PLANNING

Step 3: DESIGN LAYOUT

Basically in this step, we create the front-end part of our website with the help of languages like HTML, CSS, Bootstrap, and Javascript.

Step 4: DEVELOPMENT

Step 5: TESTING, REVIEW, AND LAUNCH

Step 6: MAINTENANCE AND UPDATION

Facilities required for proposed work

Hardware Requirements: Laptop – i3 processor or higher, 4 GB RAM or higher, 100 GB ROM or higher
Software Requirements: Laptop or PC, Windows 7 or higher, Visual Studio, HTML, CSS, Javascript, Mysql, Php

References

[1] Geekathon series(2013)[Online]. Available: http://www.GeeksforGeeks.com
[2] Jimmy Wales, Larry Sanger (2001)[Online]. Available: http://www.Wikipedia.com
[3] Refnes Data (1998)[Online]. Available: http://www.w3schools.com
[4] Steve Chen (2005) [Online]. Available: http://www.youtube.com

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Student and Faculty based University Management System C++ Project

This Project Titled “Student and faculty based  University Management System” is developed mainly for the purpose of managing All College functions like :

  • ADMINISTRATION
  • STUDENT’S INFORMATION & PERFORMANCE
  • FACULTY DETAILS

Motivation

  • In today’s time, it is very difficult to maintain the records of thousands of students manually.
  • Moreover Finding each & every small detail related to Students and Faculties Of Different Departments in a University is not at all an easy task.
  • So we designed this system which makes the work of an administrator easier and faster.

SOFTWARES

  • Code: Blocks
  • Turbo C++

Flowcharts:

Finance Module Flow Chart:

Admin Module Flow Chart:

Student & Faculty Module Flow Chart:

Applications:

  • It can be implemented in each and every university in which access can be given to all the students and faculties.
  • This could help them to be updated with all the information regarding academics and fees/salaries etc.
  • Instead of finding receipts of fees or salaries, which may take hours, now it could be easily generated in a fraction of a second.

Challenges

  • It was difficult to merge different individual classes into a single large program as it undergoes inheritance and also variable scope gave us errors.
  • We also had some problems while formatting Marksheets and Certificates as it includes various types of ASCII characters.
  • We also had File Handling as our biggest Challenge. For eg., While getting information from the file we had many errors like the number of columns not matching in File and Program.
  • Along with that Modifying Information in files like Changing Fees after Paying Pending Fees was a difficult task for us.

Conclusion

  • We Learnt How To Make Real Life Applications with C++
  • Different concepts we learned are :
  • INHERITANCE (Single & Multiple Inheritance )
  • FILE MANAGEMENT
  • LOOPING STRUCTURE (FOR and WHILE)
  • CLASSES & OBJECTS
  • other Concepts Like SWITCH, GOTO, Simple IF…ELSE

Future Scope

  • We can add Attendance Criteria which would be helpful for Teachers and Students as well.
  • We can also add Academics Section, after which students can access their subject-related materials and can also submit Assignments.
  • In This Project, Quiz Option also can be added which can help students to improve their studies, and also it can be evaluated.

Download the complete Student and faculty-based University Management System C++Project Code.

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Online Shopping Management System Java Console Application

This Project simulates the working of an online shopping portal where customers can buy products. Our Online Shopping Management System project is a purely console-based application and is implemented using the programming language JAVA.

This Java Console Application contains mainly two panels :

  1. Admin Panel – functions provided like managing products and customers
  2. Customer Panel – functions provided like buying products and making payments

A total of 8 class files have been created which are :

  • Database connection.java
  • Shop.java ( This is the main or the starting point of the project )
  • Admin.java
  • Customer.java
  • Products.java
  • Cart.java
  • Payment.java
  • Bills.java

Java Concepts used in the project are :

  • String manipulations
  • Collections framework in form of ArrayList
  • JDBC
  • Exception Handling
  • Inheritance
  • Classes and Objects
  • BufferedReader for taking entry

ROLE OF EACH MEMBER IN THE PROJECT

  • Designed class files – DatabaseConnection.java, Shop.java, Admin.java, and Customer.java and contributed to Debugging
  • Designed class file – products.java and contributed to Debugging
  • Designed class file – bills.java and contributed to Debugging
  • Designed class files – Payment.java and Cart.java and contributed to Debugging
  • Combination of class files in the end and for their joint working, Each member contributed equally.

DETAILS OF CLASS FILES

MAIN CLASS ( superclass ) = Shop.java

SUBCLASSES of Shop.java = admin.java and customer.java

Shop.java :

Main functions = registration of customer or admin, login into system Entry through buffered Reader

Array List used in login function to store id, password, and user type ( C for the customer, A for admin ) as a list

Database tables used are login info, admin info, and cast info Login info = storing used id, password, and type of user Admininfo = storing all details of admin except password Custinfo = storing all details of the customer except password setUID() function sets the admin ID to store in database setCUID() function sets the customer ID to store in the database.

Admin.java 

Functions include managing products (add,delete,view,search) by calling productsPage() function of products.java

Other functions include adding customers, removing customers, editing profiles, view registered customers.

For registering customers, since admin.java is subclass of Shop.java , registerCustomer() function of Shop is called by Shop.registerCustomer(), hence the small use of inheritance is here as the function need not be rewritten.

Customer.java :

Database table custinfo accessed for editing profile function

The main functions are viewing products, searching for products, adding and removing products from the cart, view the cart, and proceeding to the payment function.

Here first initializeProducts() function is called to store all product info in array lists, so that database need not be accessed everytime, hence Concept of collection framework is used here in form of ArrayList and through ArrayList functions .add(), .get(), .clear()

.add() = to add to ArrayList

.get(int i) = to get the element stored at index i in the ArrayList

Proceed to payment function calls payment.java class file and functions like add to cart, remove from the cart, and view cart call Cart.java. Calling is done via class objects like customerCart and p.

customerCart = object of Cart class p=object of Payment class

Customer.java is also the subclass of Shop.java where it calls the registerCustomer() function of the Shop.java through Classname.methodname like Shop.registerCustomer()

Products.java :

The main functions are added, removing, altering product info, viewing, and searching products setPid() function is used to set product id to store in the database

database table products are accessed to add, remove and alter product info

Cart.java :

This class file contains functions of the cart like add to cart, view cart, remove the product from the cart and cancel cart which is called from the customer.java class file via object.

Here add to cart function gets the product details to be added from customer.class via the constructor and adds them to the ArrayList so that the ArrayList can be used later on for displaying cart details and other functions as required.

Payment.java :

The main function of the payment class is to display bills and pay bills by calling bill.java, therefore it is an intermediate class between customer.java and bill.java and this class also stores payment details like bill and card details.

Bill.java :

Bill. class is called from the payment page through an object, this class contains details of a bill like a billing id, products purchased, and total amount. It also contains customer details whose bill it is. It stores the product details that are purchased in the ArrayList for easy access later on.

It contains functions like :

Generate bill = for calculating and storing the total amount in a variable Set bill id = for setting the bill id

Display bill = for displaying bill details

addtoDatabase = to add bill details to database table bills.

DatabaseConnection.java :

 The database connection is a class file that is used to establish a connection with the MySQL server and to create a database “Onlineshop” and five tables – login info, admin info, bills, products, and cast info. It takes the help of a flag variable to check whether the database schema exists or not and if exists, it only connects Java to MySQL.

It is imported into the class files like Shop.java and products.java where it is used to access the database and make connections.

Exceptions that are used in the project are :

  • IOException: This exception is used wherever BufferedReader has been used.
  • For handling the exceptions caused due to database like

ClassNotFoundException or SQL Exception, a try-catch block has been added.

  • For any other type of exception, sufficient try-catch blocks have been added.

Exception Handling features that are used in the project are :

Try-Catch block

Exception class functions like printStackTrace() have been used

throws keyword has been added in those methods wherever an exception is thrown and not handled by the method itself.

Snapshot of DatabaseConnection.java where Exception handling is used.

COLLECTION FRAMEWORK USED IN THE PROJECT

The concept of collections framework is used in the project through the implementation of ArrayList .

Use of ArrayList in the project :

Array List is used in the project to store the fetched results from the database for easy access later on. ArrayList is used in many class files in the project, like in customer.java , available product details are fetched from the database and stored in the ArrayList so that whenever the customer tries to access product info, it is fetched from the ArrayList and not from the database which reduces the complexity of code and saves time.

For using ArrayList Wrapper classes used are :

  • Integer for storing integers
  • Float for storing float values
  • String as a class for storing String values

DATABASE SCHEMA USED IN THE PROJECT

Here, the concept of JDBC comes into the picture and is implemented using MySQL and JAVA. The database is used in the project for storing information about admins, customers, products, bills, and login details.

Database details are as follows :

Name of the database = Onlineshop

The tables used in the project are :

  • Login Info Table
  • Admin Info Table
  • Cust Info Table
  • Products Table
  • Bills Table

How connectivity to MySQL was done :

 To connect to the MySQL server, we have used the JDBC concept and used the SQL Driver class to connect to the MySQL database. For using the SQL Driver class and other classes used for connection, java.sql.* is imported into the java class file.

Creation of the database and creation of tables were made in the DatabaseConnection.java class file and to access the database, later on, the code written was :

Class.forName(“com.mysql.jdbc.Driver);

Connection con=DriverManager.getConnection(String URL,username,password);

SOME SNAPSHOTS OF PROJECT OUTPUT

Main Page :

Customer section :

Some important points regarding the project :

  • The connector jar file should be added to the “Java project” using :
    JRE System Library > Build Path > Configure build path > Libraries > Add External JARs
  • For connecting to the database used in the project, enter the root password of MySql

Download the Complete Java Console Application Project on Online Shopping Management System

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Audio Classification On Cat’s And Dog’s Python Project

Our Audio Classification project illustrates a straightforward audio classification model supported by deep learning. we tend to address the matter of classifying the sort of sound-supported short audio signals and their generated spectrograms, from classifying dog’s audio to cat’s audio throughout model training. So as to satisfy this challenge, we tend to use a model-supported Convolutional Neural Network (CNN). The audio was processed with Mel-frequency Cepstral Coefficients (MFCC) into what is unremarkably called Mel spectrograms, and hence, was reworked into a picture. Our final CNN model achieved 89% accuracy on the testing dataset.

Project Overview :

The input to our model, in this project, is cats and associated dogs recording audio go in WAV kind. It lies below the supervised machine learning class. Thus, a dataset is also present as well as a target class. Hence, the intention here is to classify if the given input wav file is that of a cat or dog. Each of the dog and cat sounds is incredibly distinguished like in their pitch and frequency level since completely different| sounds have different sample rates. By default, Librosa mixes all audio to mono and resamples them to 22050 cycles/second at load time. For music and audio analysis, Librosa is associated ASCII text file python package. The info and the sampling rate are provided by Librosa. Audio or sound is in its raw kind, and the data provided should be pre-processed to extract significant and meaningful features so we implemented an algorithm i.e., MFCC (Mel Frequency Cepstral Coefficients) rule. Then, when audio extraction is done, the information is fed and the dataset is split into training and test set. So, after the preprocessing, a Convolutional Neural Network model is designed using tensor flow. For every code and model building, Keras API was used to implement Google colab.

Motivation

Machine learning can be used in image processing, understanding speech, and musical instruments, speech-to-text, environmental sound classification, and many more. And as for our project, we implemented a class of speech processing i.e, audio classification. Converting sound waves into audio and spectrograms which is a visual representation of frequencies with the help of function provided by machine learning.

There are many techniques to classify images as many different in-built neural networks under CNN are already there, especially if it is related to images. And it’s straightforward to extract options from pictures as a result of pictures already being available in the shape of numbers, because the formation of a picture may be an assortment of pixels, and pixels area units within the sort of numbers. When we have data as text, we use the sequential encoder and decoder-based techniques to find features. But if it is to sound recognition or audio it is more difficult compared to text because it is based on frequency and time. Therefore a proper model is to be made to extract the frequency and pitch of that audio so as to make it easier to later recognize it.

Flow Chart:

Preliminaries and Background 

Related work

Machine learning: Image classification of cats and dogs – Before a decade, in computer notion, many problems had been saturating in accordance with their precision. However, the accuracy of those troubles significantly stepped forward with the boom of deep gaining knowledge of strategies. The majority of the problems that arise from image class is that it is defined as predicting the distinct categories a photo can belong to. Hence, for the supplied enter/ photograph detection with the aim of accomplishing high precision, a state-of-the-art approach is incorporated, i.e., a convolutional neural network turned into the build for the photo category mission of puppies and cats. A dataset become given from Kaggle comprising a total of 25000 pix of each dog and cat.

Machine learning: Audio classification of different bird species – Here, the methodology and results of using deep learning to assist in the classification of birds by their sounds are presented. As birds indicate the health of an ecosystem, hence this topic is of high importance. Random Forest Classification and custom-made six CNN models from the literature were performed on a dataset of ten birds that were composed of xeno-canto.org. The highest accuracy was achieved at around 65% by the Random Forest and at about 58% for the CNN model.

conclusion and future work 

In this report, we first briefly explained the overview of this project and showed some referred project work already established. Then, we precisely illustrated our task, including the learning task and the performance task. After that, we explained the approach we are heading toward in order to classify the datasets. The approach/model we used is a neural network which is an implementation of the deep network which is a trainable model by which we were able to classify the dog’s and cat’s audio. The highest accuracy we got was 89.6%.

  1. In the future, we will try to implement the different high-level models in order to achieve much higher
  2. We’ll build a system that can directly intake a live raw
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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.

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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)

Conclusion

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.

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Covid-19 Outbreak Prediction Using Machine Learning Python Project

The aim of this Covid-19 Outbreak Prediction project is to make a model which will forecast the number of confirmed cases covid-19 virus in the upcoming days. Covid-19 is an infectious disease that is affecting a huge number of people all around the world.

This virus was first identified in Wuhan, China, and later spread throughout the world causing a pandemic that forced most countries to go into lockdown.

Various machine learning models and time series forecasting models.

The predictive model will be created using machine learning and using the dataset obtained from Kaggle. Machine learning automates the formation of analytical models. It is a branch of artificial intelligence focused on the principle that data can be learned from processes, It can find patterns and take decisions.

Time series forecasting will be used which is a type of predictive model. Time series forecasting is the use of a model centered on earlier observed values to evaluate future values. 

INTRODUCTION

The aim of this project is to make a predictive model which will predict the trajectory of the outbreak of the covid-19 virus in the upcoming days. Covid-19 is an infectious disease that is affecting a huge number of people all around the world.

It was first identified in Wuhan, China, and then later spread all over the world causing a pandemic.

Since no vaccine is developed which can be available all throughout the world, we have to take preventive measures which can stop the spread of the disease. Since a lockdown cannot last forever, we have to know how fast the spread is and how much more people will be infected.

The predictive model will be created using machine learning and using the dataset obtained from Kaggle. Machine learning automates the formation of analytical models. It is a branch of artificial intelligence focused on the principle that data can be learned from processes, It can find patterns and take decisions.

Time series forecasting will be used which is a type of predictive model. Time series forecasting is the use of a model centered on earlier observed values to evaluate future values.

PRESENT SYSTEM

Various work on this problem related to covid-19 is being done. Officials all over the world are using several outbreak prediction models for covid-19 to make informed decisions and implement relevant control measures. Simple statistical models have received greater attention from authorities among the standard models for covid-19 global pandemic prediction. One of the works suggests using SEIR models. SEIR means susceptible-exposed-infected-recovered model.

This model aims to forecast factors like the spread of a disease, the total number of infected, and the span of an outbreak, and estimate different epidemiological parameters like the number of reproductive. Such models can illustrate how the outcome of the disease can be affected by various public health measures.

PROPOSED SYSTEM 

In this project, we will first collect and evaluate the dataset. We will transform the raw data into an accessible format and visualize it using data preprocessing. Various machine learning algorithms such as Linear regression, polynomial regression, SVM, holt’s linear model, Holt’s winter model, AR model, ARIMA model, and SARIMA model are used. The tools used in this project are mainly sklean for model selection, and NumPy library which is used to work with the arrays and pandas that use a key data structure called a data frame that allows us to store and manipulate tabular data in observation rows and variable columns, matplotlib is a library of plotting that is used to plot graphs. After implementing the model, the model with the least mean square error will be considered the best-fit model.

System Design 

The dataset is first preprocessed and visualized so that it is in a usable format for analysis. After this, we model the data using Linear regression, polynomial regression, SVM, holt’s linear model, Holt’s winter model, AR model, ARIMA model, and SARIMA. Then we evaluate the model and choose the best one according to its root mean square.

The flowchart depicts the following

Dataset 

The dataset involves the collection of data from various sources.

Data Pre-processing and visualization 

In order to obtain accurate results, data preprocessing is done to check if there is any inconsistency in the data, if there is it is handled accordingly. We then visualize the data to study the pattern and trends in the data.

Model Building 

Various models are used in this project-: Linear Regression

Polynomial Regression SVM

Holt’s Linear

Holt’s Winter Model

Auto Regressive Model (AR)

Moving Average Model (MA) ARIMA Model

SARIMA Model

DATASET

In this project, the dataset is taken from Kaggle which is the Novel Corona Virus 2019 Dataset and the goal is to study the effect and spread of COVID-19 in the coming days, and conduct predictions and time series forecasting.

Hardware and Software Details 

  •  Software Details Python 3.7(64-bit) Jupyter notebook

Implementation work details  

First, the data is pre-processed and visualization is done and analyzed. Afterward, various models are used to train the data and the model with the least root mean squared error is selected as the best fit model. Various machine learning models are used and time series forecasting models such as holt’s linear model and ARIMA model are used. The dataset is obtained from Kaggle.

Real-life applications 

It can be used by the government for predicting the extent of the spread of the infectious disease and take action accordingly.

Data implementation and program execution 

The data is analyzed and visualized afterward. On different models, the data is trained and the one with the least mean square error is considered to be the best fit model and can be used for forecasting. The program is executed on a Jupyter notebook.

Output Screens 

Fig: Growth of different types of cases in India

Fig: Confirmed cases Linear Regression Prediction

Fig: Polynomial Regression Prediction for confirmed cases

Fig: SVM regressor Prediction for confirmed cases

Fig: Holts Linear Model Prediction for confirmed cases

Fig: Holt’s Winter model prediction for confirmed cases

Fig: AR model prediction for confirmed cases

Fig: SARIMA model Prediction

System Testing 

In this project, the model evaluation part is very important as by the means of it we can identify which model can best fit the problem.

Here the models are evaluated on the basis of their root mean square error(rmse).

The root-mean-square variance (RMSD) or root-mean-square error (RMSE) is a commonly used calculation of the differences expected by the model or estimator between values (sample or population values) and the values observed.

According to the rmse values of all the models tested in the project, the one with the least rmse value was the SARIMA model. So it can be considered the best fit model for this problem.

Conclusion

 It is concluded that machine learning models can be used to forecast the spread of infectious diseases like Covid-19. In the project, we used various algorithms to forecast the rise of confirmed cases. It was observed among all the algorithms used, SARIMA had the least rmse so it was considered the best fit model for the data that was available.

Limitations

It is a new virus so only a year worth of dataset is available. Generally, the more data we have the better accuracy we get and we have to keep updating the data.

Scope for future work

 It can be implemented such that it can update its graphs or predictions according to real-time values.

Download the Complete project on Covid-19 Outbreak Prediction Using Machine Learning Python Project Code & Report.

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Prediction of the growth of Corona Virus Python Project

The upsurge of this disease is CORONA VIRUS has created a life-and-death situation in the world of the living. The virus is increasing day by day and effective lives. Machine Learning can be established very effectively in tracing the disease predicting its growth and forming an effective strategy in order to manage the effect of the virus. This report gives us a full glance and the best mathematical computation with modeling for predicting growth.

In an Corona Virus Prediction ML-based project, we come up with various computations and modeling to suspect or predict the growth of a particular dataset. Although this concept can be used on a dynamic dataset that is changing day to day, here in this report we will study a particular dataset.

Working on the dataset led to various challenges such as modeling different algorithms of machine learning but finally worked on them in order to get the best result. This report is an insight into the working brief of the project such as descriptive information about machine learning, algorithms, statistical description, and most important the programming language used here which is python.

INTRODUCTION

This deadly disease is caused by the spread of various germs and harmful bacteria(pathogens) which transmits from one human to many humans, from one animal to many, and from animal to human. Early diagnoses are curable, while the patients suffering from it with a maximum number of days are not 100% curable.

There is a need for innovation in predicting the growth with deep thorough analysis, of huge global data on the rise of the virus.

The Corona Virus Prediction project comprises two main features or methods we can say, first predicting and analyzing cumulative confirmed cases and then representing with visuals that are data visualization. The second one is predicting the growth of total, confirmed, and new cases and finding accuracy.

  • PRESENT SYSTEM

Many employers are working on the same data and with the same idea of predicting the growth of the virus by analyzing cases. The COVID crisis has led many colleges and students to work in teams to get into a solution against corona.

There are many ongoing types of research and many projects have already been developed in predicting creating awareness on the same

  • PROPOSED SYSTEM

Working on the dataset led to various challenges such as modeling different algorithms of machine learning but finally worked on them in order to get the best result. It is an insight into the working brief of the project such as descriptive information about machine learning, algorithms, statistical description, and most important the programming language used here which is python. 

System Design 

System Flow Chart

Data Dictionary 

Data Pre-Processing: Our dataset needs to be pre-processed. Therefore, data pre-processing is required in this project.

Definition of Training Set: The training set is the data that the algorithm will learn from. Learning looks different depending on which algorithm you are using.

Algorithm Selection: Our project has been implemented using various algorithms such as linear regression, random forest, and decision trees.

Decision Tree: In python, we use a decision tree to observe and figure out the trained data in the structure of the tree in order for any future implementation. Decision Tree, here the target variables take continuous values called regression tree. 

Implementation Work Details 

Libraries used

Numpy

It contains among other things:

  • a powerful N-dimensional array object
  • broadcasting Functions
  • Tools for integrating
  • Useful linear algebra etc.

Pandas

Pandas is an open-source, BSD-authorized library giving superior, simple-to-utilize information structures and information investigation apparatuses for the Python programming language.

  • Benefits:

Python has for some time been incredible for information munging and planning, however less so for information examination and display. pandas help fill this hole, empowering you to do your whole information examination work process in Python without changing to a more space-explicit language like R.

Joined with the amazing IPython toolbox and different libraries, the earth for doing information examination in Python exceeds expectations in execution, profitability, and the capacity to work together.

More work is as yet expected to make Python a top-notch measurable displaying condition.

Download the Complete Project on Prediction of the growth of Corona Virus Python Project Code and Report

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Android Application for learning Internal Examination Objective-Type Questions

According to the JNTU curriculum, students are capable of scoring 25% of the overall marks based on their performance in the internal i.e. Mid-Term examinations. In the internal examinations, each subject carries 25 marks, out of which 10 marks are allotted for subjective-type questions, 5 marks are allotted for assignments and the rest of the 10 marks are allotted for objective-type questions.

Students face many difficulties, especially while preparing for the objective type questions as the existing system i.e. previous year’s objective type question papers and their keys available in pdf format on JNTU-related websites & blogs, class notes, textbooks, etc. do not provide an effective way of learning. In the existing system, the student has to visit multiple websites to view and download previous question papers and most of the question papers are in pdf format where questions are listed on one page and answers are listed on another page which makes it the student difficult to read and learn, he will have to switch to two different pages in order to read one objective type question.

The student has to repeat this to learn all the questions which are time-consuming and inefficient to learn. In order to overcome these problems, we need to develop a system that can be accessed by the students(users) easily. We strongly believe that building an application on Android can solve the problems faced by the existing system.

PROBLEM STATEMENT:

Every semester, JNTU conducts two mid-term examinations consisting of 10 marks worth of multiple-choice questions and fill-in-the-blanks. In order to secure good marks in the internal examinations, students must be prepared with 4 units of each subject. They can prepare from multiple sources including notes, textbooks, references, and previous question papers. It is difficult and time-consuming to find and download all question papers as they are located at various sites.

We have to download these question papers and the question papers are in pdf files and their respective keys are in other pdf files. Hence it is often time-consuming and difficult for the students to prepare effectively for the bit paper of mid-examination.

MOTIVATION:

The marks secured in internal examinations play a vital role in improving the overall aggregate of students. Most of the students prepare well for the theory part of the internal exams as it has efficient and direct sources to prepare like notes, guides, textbooks, etc. but sadly, they are lethargic in preparing for the bit paper of internal examination. The reason behind this is that the sources for preparing for bit papers are not clear and are difficult to use. Hence it is important to build a new system using a new approach through which the students can easily prepare for the bit papers. This Internal Examination System should also be in such a way that it should be available and accessible to the majority of students. Therefore all the above factors led to the development of an android application for learning JNTU bits. This provides its users with a user-friendly interface and an effective way of learning the JNTU bits.

SCOPE:

This Internal Examination System software will be an Android application for Learning JNTU bits. This system will be designed to maximize the scope for the users to score higher grades in Mid-term examinations. This is done by preparing the user for Multiple Choice Questions (MCQ) and Fill-in-the-blank questions worth 10 Marks. This system provides a user-friendly environment for users. It can be accessed on the go at any time by the users. It provides a user interface in a hierarchical manner, hence it doesn’t lead to any confusion. This system also contains a relational database (using SQLite) containing a list of questions (BITS) and their corresponding answers for different branches and regulations.

OUTLINE:

This Internal Examination System application serves as an effective solution for students facing difficulties in preparing for the bit paper of an internal examination. There is no need for internet connectivity in order to use this application. This application provides an efficient user interface and is easy to operate. This application supports the features of android as it is built on Android. This application operates in either of the two modes (revision mode/learning mode) based on the requirement of the user. Therefore this application is user-friendly and also provides high performance.

EXISTING SYSTEM:

According to JNTU, every semester will have two mid-term examinations (except for the first year, as it only has three mid-exam for the academic year) consisting of 10 marks worth of multiple-choice questions and fill-in-the-blanks. Students must prepare for 4 units for each Mid-Term exam. As per the existing system, students prepare themselves for objective-type questions from multiple sources including textbooks, references, and previous question papers. Previous question papers are the most important source of questions as there is a high likelihood that a few or more questions will be repeated in the exam.

These papers are accessed via webpages (and/or) other sources in the form of PDFs/word documents. This generation of students is used to a fast-paced lifestyle. As the existing system is time-consuming and it deals with patience, most of the students ignore the preparation for objective-type questions. They might rather try their luck with objective-type questions during the internal exams. Hence this affects the student’s overall performance in internal exams.

DISADVANTAGES:

  • It is difficult and time-consuming to find and download all question papers as they are located at various
  • One cannot effectively form the question papers as questions and answers are located on different pages which leads to we can overcome the flaws of the existing system by making use of the proposed system that is mentioned below.

PROPOSED SYSTEM:

The marks obtained in internal examinations play an important role in improving the percentage of students. The system will be an Android application for learning JNTU bits. It will be a handy application for the users. The user shall be able to use this system whenever and wherever he/she wants. The user shall be able to prepare for different subjects’ objective-type questions from one single source i.e. with the help of the proposed system (Android application).

This Internal Examination System will be built with the intention to be highly beneficial for the users (especially for the students). We will build this system in such a manner that it would support most versions of the Android operating system. Therefore this system is intended to provide an optimized way of learning the objective type questions to the users.

ADVANTAGES:

  • Easy to access JNTU bits which are updated
  • Scoring-system for students to learn
  • Saves time by allowing users to switch to multiple subjects within seconds

Overall Description

Product Perspective:

To provide users with an optimized way of learning JNTU bits. This is done by providing the bits (Multiple Choice Questions and Fill in the Blanks) of various subjects in the application, which can be accessed easily by the users. The efficiency of learning is increased as this application can be operated in either of the two modes (learning mode and revision mode).

Product Functions:

The Internal Examination System is intended to perform the following functions:

  • It provides branch-wise objective-type questions for the user.
  • It also provides objective-type questions based on the
  • It allows the user to easily navigate through various subject’s objective-type question papers based on his/her
  • It acts as a platform for the users to learn the objective-type questions effectively using different modes:
  • Learning mode
  • Revision mode.
  • Learning mode: In this mode, the system provides a list of questions and when the user selects a particular question then the system displays that question along with options where the user is facilitated to select an option according to his choice and submit it then the system shows whether the user answered it correctly or not in this way it provides an active, comfortable and effective mechanism towards
  • Revision mode: In this mode, the users are provided with a list of questions along with answers to revise so that it does not waste time during
  • The system consists of a scoring system, which allows the user to self-assess.

User Documentation

The Internal Examination System will consist of in-built documentation which can be accessed via the help menu. The documentation will contain information about various functions of the system and how to utilize those functions in an effective manner.

External Interface Requirements

User Interfaces:

  •  This system display’s a welcome screen as an introduction for the first time for users.
  • After that, the system navigates to a new screen where the users shall be able to set their batch details.
  • Then the user shall be able to select the subject and the mid-term.
  • Now the user shall be able to attempt a question.
  • Then the user shall be able to view the correct answer.
  • The system displays the current mode (Learning, Revision) in the title bar.
  • The system provides a single space for choosing both modes and changing settings.
  • The user shall be able to view help and information.
  • The system provides the users with a single screen where all the questions are displayed and whether it was answered correctly.
  • Tabs to switch quickly both Multiple Choice questions and fill-in-the-blanks.
  • The user shall be able to exit the application.

Software Requirements

Operating System: Android 4.0.3 and above

FUNCTIONAL REQUIREMENTS:

The following are the three main scenarios of the Internal Examination System application:

Learning Mode:

Learning mode offers the user a chance to attempt a question before revealing the correct answer to the user. This gives the user feedback on whether their answer was correct or not.

It is given high priority as it is an essential part of the system without which users cannot become better at scoring good marks in objective-type questions in the internal examinations.

  • The user should select the current batch.
  • The user should select the current mid-term.
  • The user must select the appropriate subject.
  • The system should verify that the appropriate year and corresponding semester are selected. The system does not allow users to make erroneous selections.
  • The system should verify that only subjects in that semester are displayed and selected.
  • The user can select any question from a list of all the questions in that subject.
  • The user can view and answer the questions.
  • The system should be able to provide users with the correct answers in a clear manner. REQ-9: The system should be able to provide the option of loading the next Question.

Revision Mode

In this mode, the user can browse through all the questions provided in the application.

The correct option for the question is displayed to the user.

Revision mode is a high priority and is a fast-paced mode designed to enable the users to quickly revise the answers before the exam.

  • The user should select the current batch.
  • The user should select the current mid-term.
  • The user must select the appropriate subject.
  • The system should verify that the appropriate year and corresponding semester are selected. The system does not allow users to make erroneous selections.
  • The system should verify that only subjects in that semester are displayed and selected.
  • The user can select any question from a list of all the questions in that subject.
  • The system displays the question and its correct answer.
  • The system allows the user to load the next question.

Scoring System

The scoring system displays the total number of questions answered correctly and the score for each subject. It allows a user to assess their own progress while learning and allows them to compare their progress in different subjects.

The user can measure their own performance. It is difficult to assess themselves and understand their progress without a scoring system.

  • The system stores the total no. of questions answered by the user in an array.
  • The system should check whether the user answered a question correctly or incorrectly.
  • The system should assign a score based on the number of correctly answered questions.
  • The system displays the score on a total and per-subject basis.
  • The user can reset the score if needed. When the user resets the score the system displays attempted questions and their score as zero. The user can retake learning mode to start attempting the questions from the beginning.

NON-FUNCTIONAL REQUIREMENTS:

Safety Requirements

There are not many safety requirements with this application, other than any normal hazards of a mobile device. One such hazard is users, using this system when they are driving a vehicle. And on the other hand, the users are strictly suggested to use this system only for preparation purposes before the exam but not in the examination hall during the exam.

Security Requirements

A user cannot edit any question or answer stored in the database of the application. Android provides SQLite helper classes which are implemented in such a way that only classes belonging to our system may access the database. Additionally, our system utilizes the built-in security in Android operating systems which provides security to the user’s device through a password, pin, or pattern.

Software Quality Attributes

  1. Correctness: The data of the system e. objective type questions and their respective answers are collected from genuine sources like the JNTU University, websites, faculty, etc. And it is arranged in an error-free manner. Hence the system ensures correctness to its users.
  2. Flexibility: The system layout is in such a way that it can be accessed via both mobile and
  3. Usability: This system can be used on the go by the users. It provides a high degree of usability.
  4. Reliability: The user can completely rely on this system for effective learning of the objective type

CONCLUSION AND FUTURE SCOPE

This application is developed on Android. It is used to overcome the difficulties faced by the students while preparing for the bit paper of an internal examination. It introduces a modern way of learning JNTU bits to its users. The list of questions and keys present in the application are collected from reliable sources like textbooks, downloaded from the JNTU website, etc… hence the content provided by the application is reliable. The user interface of this application is simple, hence it can be operated with ease. This application allows the students (users) to learn the bits in an effective manner and hence this will help them to improve their overall aggregate.

In the future, we need to extend this application such that it can be used by students of all branches under JNTU i.e. EEE, Civil Engineering, Metallurgical Engineering, etc. so that majority of students can be benefitted. We can also add additional features to this application like marking the most important questions, practice tests with time limits, etc… At present, if we want to update this application, we need to uninstall it and again install the new version of the application. In the future process can be made easier by making it an online application. Efficient security methods should be provided to the application as it becomes online. The application should be able to run on the forthcoming versions of Android.

Download the Complete Android Application for learning Internal Examination Objective-Type Questions Project Code.

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Food Recipe Cross-Platform Android and IOS Application Project

Surely there are many Food Recipe Cross-Platform apps that offer a wide variety of options to choose from, but most of them are online only, and those that are offline lack either that multitude of options or are devoid of proper UX and structure.

Modules and Description:

1. Smart search filters:

Users can search or filter recipes by name-based ingredients and diet. When a user searches for an ingredient, all the related recipes to that ingredient will be shown to the user, and a search based on a diet is also displayed. For example, some people are allergic to some ingredients and wish not to include them in their meals. The user will be able to search the recipe based on diet or other requirement factors. The search filter will return the appropriate recipes depending on the type of search.

Search by name, base-ingredients, diets,.

2. Categorized view:

In our application, all recipes are categorized into various cuisine, food types, diets, and varieties. We collected the exclusive authentic recipes of various countries and their cuisines are categorized on the type of food and the specific diet that the user is seeking varieties.

Recipies are categorized into cuisine, food types, diets, vegan, etc,

3. Curated Collections:

We’ve gathered recipes from famous chefs around the world and listed their signature recipes and collected various information and data about these recipes from popular websites and articles.

Curated recipe collections from famous chefs, web articles, etc.

4. Pantry Manager:

Every user can manage their own personal pantry with the help of a pantry manager, which we have included in our application.

5. Meal Planner:

Manage your pantry using our pantry manager and Plan your meals for family dinners, parties, and other social gatherings.

6. Shopping list:

Add recipes to your shopping list and browse the required ingredients with a quick glance.

We have also given a feature where the user can add ingredients to the shopping list. So that the user can track all the missing ingredients and purchase them all at once later when he/she visits for grocery shopping. These shopping list features take out all the work for you, whether you want to share a virtual list with your family and friends, or check out the nutrition facts for whatever you’re buying. It can be used and created by more than just a cell phone, as some apps can integrate with smartwatches.

 7. Skill Guide and Glossary:

Under the skill guide and glossary section, we’ll provide the user with various information on cooking techniques. Like knife skills, etc., and including a ‘Dictionary’ to understand common culinary terminology. This section is basically dedicated to starters who do not have experience and match knowledge in cooking.

Offline wiki on knife skills, cooking techniques, and various other culinary skills including dictionary to better understand common culinary terms

Activity Diagram:

  • View Recipe

The user shall be able to see the recipes in the application. In other words, the user shall be able to access the Recipe Forum, where all the user’s recipes are posted and ordered by the date of creation. Our application allows users to view recipe lists with images and titles. Single recipes can be viewed with images, recipe titles, and details of cooking directions. 

  • Search for a Recipe

 The search functionality will provide the users with a shortcut to find their targeted recipes if posted before by the users.

  • View Ingredients

The user of the app shall be able to view the ingredients of a certain recipe.

  • Add Recipe as Favourite

Once the user accesses the recipe information, he/she shall be able to mark a recipe as a favorite. Our application allows users to save recipes as favorites. When a user performs a search operation, the result is a list of recipes. Each recipe in the list has a favorite button on it. Users can add recipes by clicking on the favorite button.

The user shall be able to access the recipes that he/she marked as favorites.

 Result Screen Shorts

  • Recipes are categorized in the form of cuisines (based on countries and origin).
  • Fast food items are categorized into Burgers, Pizza, Noodles, and Sausages.
  • We have also collected the signature dishes of some of the world-famous chefs under the Chefs Curated category.
  • We have a category named ‘Meals’, were further divided into 6 categories:

– Lunch

– Breakfast

– Dessert

– Dinner

– Salad

– Soups

  • We also have a special Category. Here, the user-specific recipes are collected. For instance collection of vegan recipes is included.

External Interface Requirements:

  • Hardware

The Food Recipe Cross-Platform mobile app will be operating on Android and iOS.

  • Software

The mobile app will be compatible with the mobile and tablet(Android app) last versions.

Developer Requirements:

Hardware Requirements:

  • OS: macOS-64bit and Windows 7 (64bit) or later
  • HDD:1.23GB if windows or 2.8GB if MacOS for Flutter SDK
  • RAM:4GB (minimum)
  • Processor: Intel i3 3rdgen or AMD equivalent

Software Requirements:

  • OS: macOS-64bit and Windows 7 (64bit) or later
  • Tools: Flutter SDK
  • Android Studio/Visual Studio Code or any other IDE supporting dart
  • Git
  • Windows Powershell 5.0(only for windows users) 

User Requirements: 

  • OS: Android Jellybean or newer and iOS 8 or newer
  • Hardware: iOS devices (iPhone 4s or newer) and ARM Android devices
  • Storage Space: ~200MB
  • RAM: 2GB

Conclusion:

  • So by using this application users can cook different recipes at their homes.
  • They can also manage their diet.
  • They can manage their time.

The Food Recipe Cross-Platform application meets with the enterprise-class application principles. It is designed to be performing, scalable, extensible, and highly available.

Future Scope:

In future work, I am planning to persist in developing more mobile apps and entering deeply the world of Android development. Food Recipe Cross-Platform has helped me to gain a lot of development skills and enrich my background, as I spent the previous 4 months searching for every tiny detail that concerns the development of android applications. Thankfully, I have built a good knowledge.

Therefore, any upcoming Food Recipe project of mobile application development will undoubtedly be within my reach. In addition, as to the future of Recipe Organizer, I will deploy it in Google PlayStore and update the app from time to time if necessary. I will export my database soon and then buy an account in Google PlayStore to publish the app. Also, I will monetize my application using Admob.

Download the complete Food Recipe Android and iOS App Project Code, Report, and PPT.