The purpose of this analysis is to build a prediction model to predict whether a review on the restaurant is positive or negative. To do so$,$ we will work on Restaurant Review dataset. The dataset contains two columns, "Liked" and "Review". The liked can be $0$ or $1.$

Download the dataset and Create a dataframe named as Rest$\_$rev, then check the head, info, and describe methods on created dataframe Rest$\_$rev.$(1$ Mark$)$

Perform pre$-$processing steps like Removing Punctuations, Numbers, and Special Characters, Stop Words in dataset. $(1$ Mark$)$

Normalize review by using Stemming or Lemmatization. $(1$ Mark$)$

Preprocessed Review should be included in the Rest$\_$rev data frame as cleaned$\_$Review. Plot word cloud for all classes of the Review. $(1$ Mark$)$

Create two objects Xand Y$.$ X will be the $$cleaned$\_$Review $\text{'}$ column of Rest$\_$rev data frame and Y will be the $\text{'}$ liked' column. $(5$ Marks$)$

Create a Skip$-$gram object and split the data into training and testing sets. Train a Decision tree model. Display the confusion Matrix for both train and test.

Create a CBoW object and split the data into training and testing sets. Train a Decision tree model. Display the confusion Matrix for both train and test.

Compare Skip$-$gram and CBow. Answer without justification will not be awarded marks.

$6.$ Display the HMM POS tagging on the first row of $$cleaned$\_$Review$.(1$ Mark$)$