github-actions[bot]
commented
3 weeks ago Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. If you have any questions reach out to LinkedIn. Your contributions are highly appreciated! 😊
Note: This repo is for beginners to learn and start with Opensource we won't accept more than 10 issues from a single person, This restriction applies to Gssoc project which has a similar kind of adding folder files, Points will be reduced when we find Spam.
I Maintain the repo issue twice a day, or ideally 1 day, If your issue goes stale for more than one day you can tag and comment on this same issue.
You can also check our CONTRIBUTING.md for guidelines on contributing to this project.
Hello! I am a member of GirlsScript. I want to add a project and the objective of this project is to implement a web application for stress prediction using Django and a Naive Bayes machine learning algorithm. This web application aims to demonstrate the use of machine learning techniques in predicting stress levels based on user inputs.
Data Analysis, Cleaning, and Preprocessing:
Conducted extensive data analysis to identify key features impacting stress levels.
Cleaned and preprocessed the data to ensure quality and relevance.
Used Count Vectorizer and TF-IDF (Term Frequency-Inverse Document Frequency) to convert textual data into numerical features suitable for machine learning.
Implemented Stress Prediction Algorithm:
Utilized the Naive Bayes algorithm for its simplicity and effectiveness in classification tasks.
Developed a model to predict stress levels based on feature text, i.e user will provide the text based on the emotion they are feeling.
Used data from kaggle website for model development
Django Web Application:
Created a user-friendly web interface using Django for users to input their data.
Integrated the Naive Bayes model into the Django framework to provide real-time stress predictions.
Comprehensive Analysis:
Conducted thorough testing to evaluate the model's accuracy and performance.
Compared the Naive Bayes algorithm with other potential model i.e RandomForestClassifier to highlight its strengths and weaknesses in this context.