I’ve also noticed that the evaluation of the regression model includes classification metrics such as accuracy, precision, recall, F1 score, and confusion matrix. These metrics are specifically designed for classification tasks where the goal is to predict discrete labels. However, in project which involves Stock Price Prediction which is a regression tasks, the objective is to predict continuous values, making these metrics irrelevant and potentially misleading when evaluating model performance.

Since metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) are already implemented and are appropriate for this regression task, I suggest removing the classification metrics. This will help maintain clarity and focus on relevant performance indicators.