Executed by running ML_Stock_Movement.py

Implements Yahoo Finance's yfinance package to fetch live and historical stock daily closing price data a moving 5-year time frame. Data is retrieved as Pandas data frames Model is trained using sklearn linear regression Plots are generated demonstrating predicted vs. actual visually

validation: cd /infra/test
    % go test

cd <repo root>

Handles data ingestion and model execution. Responsibility: Generates daily stock movement forecasts.

Key Action: Ingests 5 years of historical data (including After-Hours sessions) to train a Multivariate Regression model.

Output: Saves a serialized .joblib model and creates a "Pending" entry in the local prediction_history.csv.

acts as the project's QA Manager. It provides the essential feedback loop to ensure the model stays grounded in reality.

Responsibility: Grades the accuracy of previous predictions.

Key Action: Retrieves actual closing prices for "Pending" records and compares them to the Predictor’s results.

Output: Updates the historical record with Quality Scores (e.g., Mean Absolute Error) and marks predictions as "Verified".