We need to develop a classification system using the ADFA dataset, applying multiple feature extraction techniques like Bag of Words (BoW), Skipgram, and TF-IDF. The features will be used to train a Perceptron model for classification. We will utilize Grid Search for hyperparameter optimization to improve model performance.

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.