Performance Analysis of Machine Learning Algorithms by Using WEKA and Scikit-Learn

This study compares the performance of WEKA and Scikit-learn in predicting whether a person has diabetes or not based on different machine learning algorithms. The dataset contains information on the patient's age, insulin level, blood pressure, and other relevant features. We compare the performance of algorithms such as KNN, decision trees, random forests, and logistic regression dataset, and evaluate their accuracy using various performance metrics. The results of this study can provide insights into which algorithm is most suitable for predictive analysis of the diabetes dataset and can help the user make informed decisions to improve their performance. The findings of this study can aid in selecting the appropriate tool for similar data analysis tasks. Through this research, we hope to highlight the effectiveness of WEKA and Scikit-learn in data analysis and how these tools can be leveraged to uncover insightful information that may guide individual choices.