Qualitative and Quantitative Data Analysis using Classification, and Ensemble Techniques to Optimize and Predict the Performance of Reviews

As an analyst deeply engaged in data analysis, it becomes imperative to discern the inherent nature of the data, classifying it into either qualitative or quantitative forms. Qualitative data necessitates preprocessing to facilitate predictive modeling of its outcomes. In the context of this research, we employ a movie review dataset to predict both negative and positive reviews. The realm of qualitative analysis faces a notable challenge in predictive capabilities, primarily due to the diverse sentiments expressed in various reviews. To address this challenge, we employ a diverse array of classifiers such as bagging, boosting and stacking to evaluate their performance in terms of accuracy, F1 score, and training time by selecting the best performers as an ensemble classifier. Subsequently, we identify the most effective classifier and apply ensemble techniques and stacking methodologies to optimize predictive accuracy.