Data Loading and Preprocessing: It starts by reading text data from folders, preprocesses the text data by converting to lowercase, removing punctuation, stopwords, and stemming/lemmatizing words.

Feature Extraction: TF-IDF vectorization is used to convert text data into numerical features.

Model Training: It trains multiple classifiers (Naive Bayes, SVM, Random Forest, Gradient Boosting, Decision Tree) using the TF-IDF features.

Model Evaluation: It evaluates the trained models using accuracy scores on both training and validation sets. The best model is determined based on the highest accuracy score.

Visualization: It creates a bar chart to visualize the training and testing accuracies of each classifier.

Confusion Matrix: It calculates and visualizes the confusion matrix for the best model (SVM) on the validation set.