sushantmenon1 / Depression-Detection-from-Social-Networks

The "Depression Detection from Social Networks" project uses NLP and ML techniques to detect depression in individuals based on Twitter data, achieving 80% precision. It empowers early intervention and leverages machine learning for improved accuracy.

Depression Detection from Social Networks

This project aims to develop an intelligent machine learning system for detecting depression in individuals using Twitter activity data. The system leverages natural language processing (NLP) techniques and integrates both machine learning and deep learning models to optimize performance.

Dependencies

The following libraries are required to run the project:

• nltk
• pandas
• numpy
• scipy
• seaborn
• sklearn
• itertools

Features

• Utilizes NLP techniques such as lemmatization, stop word removal, and TF-IDF vectorization.
• Compares and analyzes the performance of different machine learning and deep learning models.
• Achieves an 80% precision rate for depression detection.
• Enhances accuracy through the integration of machine learning models and neural networks.