Depression Detection using CNN

This project uses a Convolutional Neural Network (CNN) model to detect signs of depression in user-generated text. It combines multiple datasets from Kaggle, which have been preprocessed and merged for effective training.

📂 Project Structure

• Depression detection CNN.ipynb: Jupyter Notebook with data preprocessing, model training, and evaluation.
• The dataset used was compiled from multiple publicly available sources on Kaggle.

💡 Highlights

• Preprocessing steps that are done and not included in this project include emoji and noise removal, lowercasing, and tokenization.
• Model built using TensorFlow/Keras with embedding layers and 1D convolutions.
• Achieves strong performance on large-scale, multi-source text data.

📊 Dataset

The datasets were sourced from Kaggle and combined. Due to the large size (~500,000 entries), the dataset is not included in this repo.

You can download similar datasets here:

• [Kaggle Dataset 1](will provice source later)
• [Kaggle Dataset 2](will provice source later)

🔧 Requirements

• Python 3.7+
• TensorFlow
• scikit-learn
• pandas
• numpy
• matplotlib

Install dependencies:

pip install -r requirements.txt