Well Mind AI - Elevating Your Emotional Well-Being in the Modern Age

This system applies Natural Language Processing (NLP) techniques to assess a user's emotional state by analyzing their textual inputs, with a particular emphasis on detecting signs of stress and depression. It incorporates machine learning models, including Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN), to classify user input and offer personalized suggestions through collaborative filtering methods. The workflow consists of data preprocessing, feature extraction, and training an SVM model to categorize stress-related expressions, accompanied by confidence scores. Simultaneously, the k-NN algorithm recommends similar content by analyzing textual correlations within the dataset. Sentiment analysis is conducted using the TextBlob library to gauge emotional polarity, offering insights into stress and depression levels. Visual tools such as bar graphs and word clouds are employed to enhance clarity and understanding of the results. Furthermore, the system supports mental wellness by delivering customized resources like soothing music and videos. This integrated solution empowers users to recognize, interpret, and address their mental health conditions through smart text-based evaluation.