1. Preprocess.scala Purpose: Handle data loading and cleaning. Should do: Load IMDb datasets from the data/ directory. Clean missing or invalid data. Transform datasets into a format suitable for KNN, such as converting genres and runtime into numerical features. Output: Returns a processed DataFrame for model training.

2. TrainModel.scala Purpose: Implements the KNN algorithm to predict movie ratings. Should do: Accept the preprocessed DataFrame from Preprocessing.scala. Compute distances between movies based on features (e.g., genres, runtime, etc.). Use the nearest neighbors to predict ratings for movies. Output: Stores the predicted ratings for evaluation.

3. EvalModel.scala Purpose: Evaluate the accuracy and performance of the KNN predictions. Should do: Compare predicted ratings to actual ratings from the dataset. Calculate evaluation metrics, such as: Mean Absolute Error (MAE) Root Mean Square Error (RMSE) Generate a summary of model performance.

4. Main.scala Purpose: The main entry point for running the project pipeline. Should do: Initialize the Spark session. Call the functions from Preprocess.scala, TrainModel.scala, and EvalModel.scala in sequence. Print the results or save outputs for further analysis.