You are provided with a dataset containing textual descriptions and associated rating data for various movies and series. Task Requirements: Write a Python script that performs the following operations: 1. Data Loading a. Load the dataset series_movies_descriptions.csv into a Pandas dataframe. b. Display the first few rows of the dataframe to confirm successful import. (3 Marks) 2. Exploratory Data Analysis a. Visualise the distribution of the ratings using an appropriate chart (e.g., histogram or bar chart). (2 Marks) b. Display the count of each unique rating to understand how ratings are distributed across the dataset. (2 Marks) 3. Sentiment Analysis a. Use a suitable natural language processing library (e.g., TextBlob, VADER) to compute sentiment polarity scores for each movie or series description. b. Add a new column to the dataframe called 'Polarity' to store the sentiment score of each description. (5 Marks) 4. Classification of Sentiment a. Based on the polarity value, classify each description as either 'Positive' (if polarity > 0) or 'Negative' (if polarity 0). b. Add this classification as a new column in the dataframe called 'Sentiment Label'. (6 Marks) 5. Visualisation a. Plot a chart (e.g., bar chart or pie chart) that shows the distribution