Movie Ratings

Requirements Clarification & Assessment

1. Understanding the Problem:

   • Objective: The task is to predict the star rating (1 to 5) a user might give to a movie on Netflix.
   • Nature of Data: The ratings are discrete but ordinal, meaning there is a natural order to them.
   • Key Question: Is the task better approached as a regression or classification problem?

2. Data Characteristics:

   • Input Features: Could include user demographics, movie attributes (genre, director, cast), historical ratings, and user activity data.
   • Output: Predicted star rating, which is an ordinal value between 1 and 5.

3. Success Metrics:

   • Accuracy: How close is the predicted rating to the actual rating?
   • Error Metrics: RMSE (Root Mean Squared Error) is commonly used for regression tasks.

4. Constraints & Assumptions:

   • The model should handle large-scale data efficiently.
   • Consideration of user privacy and data security.
   • Assumption that past user behavior is indicative of future preferences.

5. Stakeholders:

   • Netflix's data science team, product managers, and end-users.

6. Tools & Technologies:

   • Machine Learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
   • Data processing tools like Pandas and NumPy.