Project Description: Stress, tension, and misery are undermining the psychological well-being of individuals. Each individual has a justification behind having an unpleasant life. Individuals frequently discuss their thoughts via web-based entertainment stages like on Instagram as posts and stories, and on Reddit through requesting ideas about their life on subreddits. In the beyond couple of years, many substance makers have approached to make content to assist individuals with their psychological wellness. Numerous associations can utilize pressure discovery to find which virtual entertainment clients are focused on to rapidly help them. Stress discovery is a difficult undertaking, as there are so many words that can be utilized by individuals on their posts that can show regardless of whether an individual is having mental pressure. The dataset I’m utilizing for this errand contains information presented on subreddits related on emotional wellness. This dataset contains different emotional well-being issues shared by individuals about their life. People often share their feelings on social media platforms. Many organizations can use stress detection to find which social media users are stressed to help them quickly.
Objective: Developed an ML -based project to automatically detect stress from text inputs. Technologies Used: Python, Pandas, NLTK, Matplotlib, WordCloud, Scikit-learn. Key Responsibilities: Implemented data cleaning and preprocessing techniques. Utilized NLTK for text normalization and feature extraction. Trained a Bernoulli Naive Bayes model for stress detection. Generated word cloud visualizations to analyze textual data. Achievements: Successfully developed a functional stress detection model with high accuracy. Impact: Improved mental health support efforts by providing a tool for early stress detection.