Predicting stock market prices accurately is a major task for investors and traders seeking to optimize their decision-making processes. This research focuses on the comparative analysis of advanced machine learning (ML) techniques, particularly, the Long Short-Term Memory (LSTM) model and Autoregressive Integrated Moving Average (ARIMA) model for predicting stock market prices. The study enforces thorough data collection and preprocessing to ensure the quality and reliability of the historical stock price data, forming a robust foundation for the predictive models. The core contribution of this paper lies in its systematic and comparative analysis of these two models. A range of performance metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), are employed to assess and contrast the predictive accuracy and efficiency of the LSTM and ARIMA models. The research findings indicate that the ARIMA model, contrary to expectations, outperforms the LSTM model in this study, achieving lower RMSE and MAE values. Specifically, the ARIMA model demonstrates a Test RMSE of 4.336 and a Test MAE of 3.45926, indicating its superior predictive accuracy compared to the LSTM model. Furthermore, the study sets its findings against the backdrop of existing literature by comparing the performance of its models with those reported in previous research. This comparison shows better results achieved by our stock market prediction models. By addressing limitations observed in prior studies and demonstrating practical applicability, this research contributes to advancing stock market prediction methodologies, offering valuable insights for investors and traders.

In this digital world, technology is changing every second. For this reason, tech-based educational departments should be aware of their strong and weak areas. By doing so, they can analyze their performance and emphasis the respected fields to prepare their students for the challenging job market and higher studies. In this research, a novel machine learning model is proposed to predict the performance of computer science department students by analyzing their academic statements. The academic areas of the departments are divided into fifteen fields, each of which is categorized as Excellent, Very Good, Good, Average, and Bad. One thousand students’ academic statements were used as the dataset. The prediction outputs are labeled into multiple categories, with multiclass data in each. Different models are developed using multiclass-multioutput classification algorithms like Decision Tree (Tree), Extra Tree (Tree), Extra Trees (Ensemble), K-Neighbors (Neighbors), Radius Neighbors (Neighbors), and Random Forest (Ensemble) Classifier. From the developed models, the model that used "Random Forest Classifier (Ensemble)" provided the best results, and the accuracy was almost 94%. Finally, a comparison of our research with previous and existing different developed models is presented to show the novelty of this research.