StackOverflow Tag Prediction

This project aims to predict tags for StackOverflow questions using machine learning techniques. The goal is to assist users in categorizing their questions more accurately and efficiently.

Project Structure

• data/: Contains the dataset used for training and testing the model.
• notebooks/: Jupyter notebooks for data exploration, preprocessing, and model training.
• src/: Source code for data processing, feature extraction, and model implementation.
• models/: Saved models and related files.
• results/: Evaluation results and performance metrics.
• readme: Project documentation.

Installation

To set up the project, clone the repository and install the required dependencies and run the models:

git clone https://github.com/yourusername/StackOverflow-Tag-Prediction.git
cd StackOverflow-Tag-Prediction
pip install -r requirements.txt
python src/main.py

Usage

To train the model, use the following command:

python main.py

To predict tags for new questions, use:

python predict.py --question "Your question here"

Model

The model is built using natural language processing (NLP) techniques and machine learning algorithms. Details about the model architecture and training process can be found in the model directory. (Will be shared on request, reach out to choudhari.pra@northeastern.edu)

Results

The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score. Detailed results and analysis can be found in the results directory.

Model Performance

Model	Accuracy
Logistic Regression	0.84
Random Forest Classifier	0.81
SVM	0.85
Neural Networks	0.81
DistilBERT	0.87