Update requirements.txt

requirements.txt

@@ -1,25 +1,25 @@
 # Core Libraries for Machine Learning
-scikit-learn==1.5.1          # Essential library for machine learning models (Random Forest, Decision Trees, etc.)
+scikit-learn==1.6.1          # Essential library for machine learning models (Random Forest, Decision Trees, etc.)
 numpy==1.26.4                # Numerical operations (required for model input/output processing)
-pandas==2.2.2                # Data manipulation and preprocessing
+pandas==2.2.3                # Data manipulation and preprocessing
 
 # Plotting and Visualization Tools
-matplotlib==3.9.2            # Visualization library (used for plotting confusion matrices)
+matplotlib==3.10.1            # Visualization library (used for plotting confusion matrices)
 seaborn==0.13.2              # Advanced data visualization, helpful for heatmaps (confusion matrix)
 
 # Saving and Loading Models
 joblib==1.4.2                # For saving and loading machine learning models (used for Random Forest, Decision Trees, etc.)
 
 # Hugging Face Hub Integration
-huggingface_hub==0.29.0rc7      # Integration with Hugging Face Hub (for model uploading, downloading, sharing)
+huggingface_hub==0.30.2      # Integration with Hugging Face Hub (for model uploading, downloading, sharing)
 transformers==4.26.1         # Hugging Face Transformers library (for model usage on the Hub)
 
 # Optional - Jupyter Notebooks for Model Development and Experimentation
-notebook==7.2.2             # For running Jupyter Notebooks in your project
+notebook==7.2.2              # For running Jupyter Notebooks in your project
 
 # Optional - TensorBoard for Visualizing Training Process (if applicable to larger models)
-tensorboard==2.10.1          # For tracking and visualizing model training
+tensorboard==2.19.1          # For tracking and visualizing model training
 
 # Extras for performance and speedups
-xgboost==2.1.4               # Gradient boosting library (optional, if you want to use advanced tree-based models)
+xgboost==3.0.0               # Gradient boosting library (optional, if you want to use advanced tree-based models)
 lightgbm==4.6.0              # LightGBM for fast gradient boosting (optional, for high performance)