1.The main purpose of the project is to detect stress levels in textual data using machine learning techniques.
2.By analyzing text from various sources, the project aims to identify and classify stress-related content, providing insights into mental health states based on language usage.
These libraries are used for data manipulation and numerical operations. Pandas is utilized to load and handle the dataset, while NumPy provides support for numerical operations.
This library is used for sentiment analysis. It provides tools to perform common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.
The Natural Language Toolkit is used for various NLP tasks including tokenization, stemming, and stop words removal. It provides a suite of libraries and programs for symbolic and statistical natural language processing.
This library is used for machine learning tasks. It includes tools for data preprocessing, model building, training, and evaluation. Algorithms like Multinomial Naive Bayes and Decision Tree Classifier are employed for classification tasks.