Classify data using ADFA dataset with Bag of Words, Skipgram, TF-IDF, and Perceptron model, with grid search for model optimization

Requirements: Feature Extraction: Bag of Words (BoW): Extract features from text data using the BoW method. Skipgram: Implement Skipgram-based word embeddings to capture contextual relationships in the data. TF-IDF: Extract relevant features based on term frequency and inverse document frequency to emphasize significant terms. Model: Train a Perceptron model for classification of the data based on the features. Grid Search: Use Grid Search for hyperparameter optimization to improve model accuracy and performance. Dataset: Train and test the models using the ADFA dataset, which contains labeled data for anomaly detection and classification tasks. Performance Metrics: Evaluate the model using standard classification metrics such as accuracy, precision, recall, and F1 score.