A Data Scientists

Submit all your answers in one notebook file as (final_yourname.ipynb)Question 1 (80 pts)Sentiment Analysis helps data scientists to analyze any kind of data i.e., Business, Politics, Social Media, etc., For example, the IMDb dataset “movie_data.csv” file contains 25,000 highly polar ‘positive’ (12500) and ‘negative’ (12500) IMDB movie reviews (label negative review as ‘0’ and positive review as ‘1’).Similarly, “amazon_data.txt” and “yelp_data.txt” contain 1000 labeled negative review as ‘0’ and positive review as ‘1’For further help, check the notebook sentiment_analysis.ipynb in Canvas and also explore the link: https://medium.com/@vasista/sentiment-analysis-using-svm338d418e3ff1Answer the following:a) Read all the above data files (.csv and .txt) in python Pandas DataFrame. For each dataset, make 70% as training and 30% as test sets.b) By using both CountVectorizer and TfidfVectorizer separately of the sklearn library , perform the Logistic regression classification in the IMDb dataset and evaluate the accuracies in the test set.c) Classify the Amazon dataset using Logistic Regression and Neural Network (two hidden layers) and compare the performances and show the confusion matrices.d) Generate classification model for the Yelp dataset with K-NN algorithms. Fit and test the model for different values for K (from 1 to 5) using a for loop and record and plot the KNN’s testing accuracy in a variable (scores).e) Generate prediction for the following reviews based Logistic regression classifier in Amazon dataset: Review1 = “SUPERB, I AM IN LOVE IN THIS PHONE” Review 2 = “Do not purchase this product.My cell phone blast when I switched the charger”