Stress Level Prediction is a web app using machine learning to estimate user stress levels. It takes inputs like anxiety, sleep quality, and academic performance, then predicts stress using a Decision Tree Classifier. Built with Python, Flask, and scikit-learn, it's useful for students, researchers, and those interested in stress management.

The proposed project aims to build machine learning algorithms which detect the stress level of people by analyzing their behavior in different aspects of their life from the data set which was collected from different peoples.These algorithms can identify,when an individual may struggle with stress and suggest some solutions to them. We projected the system for stress prediction of people based on the Random Forest and decision tree algorithm and accuracy,precision,recall and f1score is used as a evaluation parameter.The data set consists of 18 different attributes on which different machine model will be deployed and the performance of the model can be analyzed with the help of accuracy.The random forest model has predicated the mental stress level with an highest accuracy of 62.7% among all the models.

"Human Stress Detection and Prediction using Artificial Neural Networks (ANN)" is a fascinating project where I have used machine learning techniques, specifically ANNs, to predict and detect stress levels in humans based on physiological data.

Large number of patients related data is stored and maintained in the health industry. Heart disease is the most common one nowadays. The different ways of predicting it are Electrocardiogram (ECG), stress test, and Heart MRI. Here, the proposed model uses 13 parameters for the prediction of heart disease that includes heart rate, chest pain, cholesterol level, blood pressure, Age etc. The aim of this model is to predict whether heart disease is present or not using the various machine learning models such as Decision Tree, Random Forest, Logistic Regression, Naïve Bayes. We have achieved 0.3312 log loss using the Logistic Regression.

Created dataset from scratch, cleaned with Pandas, trained Logistic Regression & Random Forest, and visualized results using confusion matrix and feature importance. Simple end-to-end project for mental health analytics.

Analysis and prediction of stress levels using data visualization and machine learning models

A machine learning model that predicts Depression, Anxiety, and Stress levels based on questionnaire responses. The system uses a neural network model and provides predictions through a simple interactive interface.

The Sleep Disorder Prediction project uses machine learning to predict potential sleep disorders based on factors like age, gender, occupation, sleep duration, quality, physical activity, and stress levels. By analyzing these inputs, a LightGBM model with 90.67% accuracy forecasts conditions .

An AI-powered Gradio web app that predicts mental health risk levels based on lifestyle inputs like sleep, stress, screen time, and more. The app uses a trained machine learning model and provides downloadable logs and a visual report of user predictions.

Over the last two decades, heart disease, also regarded as cardiovascular disease, is the major cause of death worldwide. Conferring to the World Health Organization, over 17.9 million individuals have died every year as a result of coronary heart disease, with coronary stroke responsible for 80% of all fatalities. Deaths in large numbers are frequent in low and middle-income countries. Heart disease is driven by a range of factors, including private and job-related practices, as well as inborn predisposition. Smoking, heavy alcohol and caffeine consumption, stress, and insufficient physical activity, as well as health sedentary lifestyles, hypertension, increased cholesterol levels, and pre-existing heart disorders, are all risk factors for heart disease. The capability to diagnose heart disease early and precisely plays a critical part in taking preventive steps to avoid fatalities. In certain cases, heart disease can be completely cured by a mixture of dietary changes, medicine, and if required surgery. With the proper treatment, the symptoms of heart failure can be reduced and the heart's rhythm can be improved. The estimated outcomes can be used to avoid and therefore reduce the cost of surgical care as well as other costs. My work's ultimate goal would be to reliably forecast using various attributes and come up with a solution that will assist them in avoiding potential losses. To come up with an accurate prediction and observations about the person's well-being, this prediction model will use a data science life cycle, along with machine learning models.

Supervised regression project using Linear Regression and Random Forest to predict stress levels from lifestyle data.

Stress has become a major concern due to academic, personal, and environmental factors. This project predicts stress levels using machine learning by analyzing psychological, social, and lifestyle features. The model classifies stress as low, medium, or high to support early detection and effective stress management.

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A simple OCR project that uses OpenCV and k-Nearest Neighbors (kNN) to recognize handwritten digits by splitting a digit grid image into samples, training a model, and achieving high accuracy in digit classification.

it is about stress level prediction

A machine learning project that predicts student stress levels (Low/Moderate/High) using a dataset of 1100 records and 21 features. Several ML models were tested, with Random Forest giving the best accuracy. The model is deployed using Streamlit to provide real-time predictions based on psychological, health, and lifestyle factors.

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Engineering Mini Project

Machine learning model to predict student stress levels using Python, data preprocessing, and predictive analytics

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A live dataset on mental stress features different age groups for age group prediction using random forest and decision tree models. The goal is to identify high mental stress age groups accurately.

This project focuses on analyzing and predicting stress levels among college students using machine learning techniques. The dataset is collected through a survey incorporating clinically validated questionnaires such as PHQ-9 (Depression Scale) and GAD-7 (Anxiety Scale), along with lifestyle, academic, and demographic factors.

The proposed project aims to build machine learning algorithms which detect the stress level of people by analyzing their behavior in different aspects of their life from the data set which was collected from different peoples.These algorithms can identify,when an individual may struggle with stress and suggest some solutions to them. We projected the system for stress prediction of people based on the Random Forest and decision tree algorithm and accuracy,precision,recall and f1score is used as a evaluation parameter.The data set consists of 18 different attributes on which different machine model will be deployed and the performance of the model can be analyzed with the help of accuracy.The random forest model has predicated the mental stress level with an highest accuracy of 62.7% among all the models.

STRESS LEVEL PREDICTION USING MACHINE LEARNING

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Prediction of Nurse Stress Levels Using Machine Learning Techniques

Prediction of Developers’ Stress Levels Using Supervised Machine Learning Models

Student Stress Level Prediction Web App using Machine Learning and Streamlit

📌 Sleep Quality Prediction using Machine Learning 💤 This project aims to analyze and predict sleep quality based on various lifestyle and physiological factors. Using machine learning models, we explore relationships between sleep duration, stress levels, exercise, heart rate, and other key metrics.

A stress detection system using sensor data (GSR, accelerometer, heart rate, temperature) with machine learning for real-time stress level prediction.