Part 1: Data Cleaning with Pandas

Objective: Learn how to clean and preprocess stock price data for analysis and modeling.Clean and preprocess a dataset containing IMDb's top Netflix movies and TV shows. The dataset contains information like movie titles, genres, ratings, and more. Your goal is to clean the data, extract meaningful features, and prepare it for analysis. After cleaning, you are expected to identify trends or patterns in the data.

https://drive.google.com/file/d/16zx1XQK5kTt-HvYWtozxPgPu--bTxga3/view?usp=sharing

Expected Deliverables:

• A cleaned dataset saved in a new file.
• Feature extraction and exploratory analysis with insights on the dataset.
• A brief summary of the cleaning process and challenges faced.

Part 2: Sentiment Analysis

Objective: Learn how we use Textblob for sentiment analysis. Perform sentiment analysis on a dataset containing text reviews. Use TextBlob to analyze and classify the sentiment of each review into positive, neutral, or negative categories. The focus is on understanding the sentiment distribution and identifying patterns in the text data.

https://drive.google.com/file/d/1SXZM8Xamhcz1qjDnHIz_yqTKLmNjBgAR/view?usp=drive_link

Expected Deliverables:

• A column in the dataset indicating the sentiment classification.
• A summary of the overall sentiment distribution (e.g., percentage of positive, negative, neutral).

<aside> An Important Note: Please ensure you document all steps of your data cleaning and sentiment analysis process. This includes explaining any assumptions made, handling of missing values, and the rationale behind your preprocessing decisions. Good documentation is crucial for reproducibility and will be considered in the evaluation.

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Submission

Submit the week 1 assignment here https://docs.google.com/forms/d/e/1FAIpQLSdYDxIm1J5AK9_HuU5m3iMjFXOSDw9thesN-fY-ZfeXOlDJhg/viewform