Model names:[names = ["Decision Tree", "AdaBoost", "Random Forest", "Support vector Machine", Neural Net"] answer 2.1 and 2.2 but used all the models' names provided.

2.1 Perform 3 types of classification analysis demonstrated in the course. At least one type must be a deep learning model using either a pre-trained or data-trained embedding layer. [64] X=reviews[['New_reviewText'] y=reviews[ 'Sentiment' ] X_train, X_test, y_train, ytest =train_test_split (X,y, test_size =0.25, random_state =321 ) [55] \# Calculate vocab_size from the tokenizer vocab_size = len ( tokenizer. . word_index) +1 print(vocab_size) 12163 [ ] 2.2 Evaluate the models on both the training and testing sets to obtain both performance and goodness of fit