• Early detection of mental health issues allows specialists to treat them more effectively and it improves patient’s quality of life.
  • Mental health is about one’s psychological, emotional, and social well-being. It affects the way how one thinks, feels, and acts.
  • Mental health is very important at every stage of life, from childhood and adolescence through adulthood.
  • This study identified five machine learning techniques and assessed their accuracy in identifying mental health issues using several accuracy criteria.
  • A person’s mental well-being is his or her mental condition, as well as an overview of his or her general environment. Brain chemistry abnormalities are the cause of mental illness.
  • An individual’s mental health serves as a barometer for properly addressing his or her diseases.
  • To predict any health-related irregularities, it is critical to keep track of diverse groups’ mental health profiles.
  • The community is made up of working professionals, college students, and high school students.


  • There has been many studies and researches where people have been predicting mental health problems like depression and anxiety using the algorithms of machine learning.
  • Majorly decision tree, support vector machine, random forest and convolution neural network for the collection and classification of data from blog posts.


  • The initial step is data collection.
  • We have tried to collect data from different places. There was no standard dataset available which could match our requirements. Hence, we had to collect all the data ourselves.
  • We made a survey form for each disease and distributed, both online and offline for people to fill it.
  •  The nature of our questions was objective and situational.
  • We also included people who are currently suffering from some kind of mental illness and are seeing doctors for it and taking some kind of medications.
  • Once the data collection is done, the user’s response is converted using numeric values of 0 to 3, and in some cases 0 to 4.
  • Once we had enough data collected, it was moved to preprocessing and is split into two subsets i.e., training and test data sets.
  • It is important to fill out the missing values in the dataset or modify it to increase the quality of the dataset.
  • Once the preprocessing of data is completed, it then moved to feature extraction thenceforth prediction of mental illness.



Software Requirements:

  • Front End – Anaconda IDE
  • Backend – SQL
  • Language – Python 3.8

Hardware Requirements

  • •Hard Disk: Greater than 500 GB
  • •RAM: Greater than 4 GB
  • •Processor: I3 and Above

Including Packages


  • * Base Paper
  • * Complete Source Code
  • * Complete Documentation
  • * Complete Presentation Slides
  • * Flow Diagram
  • * Database File
  • * Screenshots
  • * Execution Procedure
  • * Readme File
  • * Addons
  • * Video Tutorials
  • * Supporting Softwares

Specialization =======================

  • * 24/7 Support * Ticketing System
  • * Voice Conference
  • * Video On Demand 
  • * Remote Connectivity
  • * Code Customization
  • * Document Customization 
  • * Live Chat Support

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