निरोधः is the Sanskrit word for cessation. This project aims towards the cessation or nirodha of stress.
In the realm of mental health, understanding stress levels is crucial for fostering well-being. Nirodhah steps in as an AI-driven solution, predicting stress levels for individuals without a history of stress assessments. Leveraging logistic regression, the model achieves an impressive accuracy of 92.76%.
Nirodhah is dedicated to promoting mental health by providing a tool for predicting stress levels. In a world where stress affects everyone differently, our project focuses on creating an inclusive solution accessible to all.
Our innovative AI solution harnesses the power of logistic regression to analyze user data and predict stress levels accurately. The model considers various factors, creating a robust and adaptable tool for dynamic stress scenarios.
The core of Nirodhah's predictive capability lies in a logistic regression model. Meticulously trained on a large dataset, the model excels in evaluating stress levels with a high degree of precision. Data preprocessing and cleaning ensure the model's reliability and effectiveness.
Kaggle Dataset Exploration: Dataset Used
To empower our AI with comprehensive insights, we utilized a diverse dataset. Our exploration involved understanding correlations and attribute combinations, providing a nuanced understanding of stress factors. This exploration laid the foundation for a solution that not only predicts stress but also adapts to individual experiences.
Built on Flask, Nirodhah boasts an intuitive web platform designed for ease of use. Users, regardless of technical expertise, can seamlessly interact with the stress prediction model.
The frontend of Nirodhah is a testament to our commitment to accessibility. Through thoughtful design and meticulous implementation, we've made interacting with stress prediction algorithms effortless for users.
Transitioning from development to deployment, we selected Render as our hosting platform. Render's scalability and user-friendly environment provide an ideal setting for our Flask app. This transition ensures that Nirodhah's stress prediction capabilities are not confined to labs but accessible to anyone, anywhere.
- Ayushi Dubey (Email Id, GitHub Profile Link)
- Advika Thakur (Email Id, GitHub Profile Link)