ASL Recognition with XGBoost

Overview

This project involves recognizing American Sign Language (ASL) characters using machine learning. We preprocess the dataset, handle class imbalances, and use the XGBoost classifier to train a model that can predict ASL letters based on image data.

Features

• Data Preprocessing: Cleaned the dataset to handle imbalances, removing classes with fewer than 2 samples.
• Label Mapping: Converted categorical labels to continuous integer labels for model compatibility.
• Model Training: Trained an XGBoost classifier to predict ASL characters.
• Evaluation: Used accuracy to evaluate the model's performance.
• Model Saving: Saved the trained model and label mapping for future inference.

Requirements

• Python 3.7+
• Libraries:
  • xgboost
  • numpy
  • sklearn
  • pickle
  • collections