Canteen Automation System using NLTK and Machine Learning

The canteen automation system project is designed to select the food items from a web application with cost, time of cooking, and give rating for products. This application is designed to help students to order food items without giving orders to waiters or going to the counter and giving orders. Most of the colleges don’t have order-taking system students should directly reach the counter and give an order which is time taking process in order to solve this problem this online order-booking system is designed.

As there will be many students who will be giving orders from different departments as a web application is designed with multiple admins, each department will have one admin who will take request and process request. Another problem is best food from today’s canteen menu can be known by checking ratings given by other users based on that students can give orders. Students can also give reviews for each food item along with ratings. NLT is used to calculate the sentiment of each review by taking the yelp dataset and applying machine learning and NLTK to calculate sentiment and store it in the database.

Proposed system:

  • In the proposed system food ordering is done online and each department has its own admin who handles requests on daily basis, users can give a rating of food items which will help other students to select the food item from the list. Sentiment analysis using Yelp data set and NLTK and Machine learning are used to store the sentiment of each review given by the student.


  • Helps students to give orders from any location inside the campus and save time by reaching the canteen based on the given cooking time from the application.
  • Sentiment analysis is done for reviews using NLTK and Machine Learning. Sentiment and Rating are useful for students to select food items.


 Operating system:  Windows XP/7/10

  • Coding Language:  Html, JavaScript,  
  • Development Kit:  Flask Framework
  • Database:  MySQL
  • Dataset:  YELP
  • IDE:  Anaconda prompt

Stress Detection from Sensor Data using Machine Learning

Stress is commonly defined as a feeling of strain and pressure which occurs from any event or thought that makes you feel frustrated, angry, or nervous. In the present situation, many people have succumbed to stress especially the adolescent and the working people. Stress increase nowadays leads to many problems like depression, suicide, heart attack, and stroke. The current technology, using Galvanic skin response (GSR), Heart rate variability (HRV), and Skin temperature are being used individually to detect stress.

In this project data set is created using five features age, gender, body temperature, heartbeat, and blood pressure, and four stages of labels are used for detecting the level of stress.  A decision tree algorithm is used to train the data set and create a model and use the Flask framework to take input data and predict the stress level of the user. 


 Existing systems were designed to detect stress by taking tweets as input from the Twitter or Facebook data set and machine learning algorithms are applied to detect stress from tweets.


  • Most of the existing system works were on social networking stress data not on body-based sensor data.
  • Stress level is calculated based on tweets posted by users.


The proposed system is designed by collecting data from sensors and preparing data set on three features (temperature, heartbeat, age, male or female). Using this data set machine learning Decision tree algorithm is applied using and the model is saved. Front end web application is designed to collect new user features and passed them to the model to predict stress stages which are divided into 4 stages.


  • Data is collected from real-time sensors and a data set is created for different ages and male and female users.
  • Data is trained using machine learning which helps automate the process of stress detection.
  • The web applications can help users to easily check their stress state based on their features.

Data collection:

  • In this state data is collected from real-time sensors and stored in an excel sheet with five features age, gender, temperature, heartbeat, and this data is applied for machine learning, and a model is created.

Data preprocessing:

  • Features are extracted from the data set and stored in the variable as train variable and labels are stored in y train variable. Data is preprocessing by standard scalar function and new features and labels are generated. 

Testing training:

  • In this stage, data is sent to the testing and training function and divided into four parts x test train, and y test train. Train variables are used for passing to the algorithm whereas tests are used for calculating the accuracy of the algorithm. 

Initializing Decision tree Algorithm:

  • In this stage, the decision tree algorithm is initialized and train values are given to the algorithm by this information algorithm will know what are features and label. Then data is modeled and stored as a pickle file in the system which can be used for prediction. 

Predict data:

  • In this stage, new data is taken as input and trained models are loaded using pickle and then values are preprocessed and passed to predict function to find out a result which is shown on the web application.


 Operating system:           Windows XP/7/10

  • Coding Language:           Html, JavaScript,  
  • Development Kit:        Flask Framework
  • IDE:           Anaconda prompt
  • Dataset:          Stress dataset

File Security Using Elliptic Curve Cryptography (ECC) in Cloud


Data security in cloud computing is a mostly researched topic that has various solutions like applying encryption to data and using multi-cloud environments. But still, there are many issues related to data security. In this project, we are using ECC digital signature method to sign the signature of user data while uploading to the cloud and use the same digital signature to download when required.

Elliptic Curve Cryptography (ECC) is a modern family of public-key cryptosystems, you can use an Elliptic Curve algorithm for public/private key cryptography. To be able to use ECC; cryptographic signatures, hash functions and others that help secure the messages or files are to be studied at a deeper level.

It implements all major capabilities of the asymmetric cryptosystems: Encryption, Signatures, and Key Exchange The main advantage is that keys are a lot smaller. With RSA you need key servers to distribute public keys. With Elliptic Curves, you can provide your own public key.

In python, the above-described method can be implemented using the   ECDSA Algorithm. 


  • Using public key cryptosystems with both public and private keys can give security for data compared to single key encryption. In this project, the ECC algorithm is used for securing data to the cloud and uploading data to the cloud.

Existing system:

  • AES and DES are mostly used cryptographic algorithms for securing data. These methods are used in most of the applications which use single keys for encryption and decryption.


  • These methods are old methods that are used in most applications.
  • They use a single key for encryption and decryption.

Proposed system:

  • In a cloud environment data security is very important as data is stored in third-party servers there is a need for effective multi-key encryption techniques like ECC algorithms. In this project, we are using the ECC algorithm in python language and using the cloud to store encrypted data.


  • The time taken for the encryption process is less
  • Multiple keys are used for the encryption and decryption process.


Software Requirement: 

  • Operating system: Windows XP/7/10
  • Coding Language:  Html, JavaScript,  
  • Development Kit:  Flask Framework
  • Database: SQLite
  • IDE: Anaconda prompt