This paper has shown promising results such as Performance Comparison, Impact on Classification Performance, Computational Efficiency and Strengths and Limitations in improving the performance and interpretability of classification models. We discuss an empirical evaluation of various feature selection algorithms for classification in machine learning. We compare the effectiveness and efficiency of several popular feature selection techniques, assess their impact on classification performance, and discuss their strengths and limitations. Our findings provide insights into the strengths and weaknesses of different feature selection methods, aiding practitioners in selecting the most suitable approach for their specific classification tasks.