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1 Jul 01, 2024
An Efficient Machine Learning Technique for Rating Prediction Of Google Play Store Apps

The vast landscape of mobile applications, user ratings play a crucial role in determining an app's success and popularity. Predicting these ratings accurately not only aids users in making informed decisions but also assists developers in improving their apps. This paper presents an efficient machine learning technique tailored for rating prediction of Google Play Store apps. Our approach leverages a combination of advanced machine learning algorithms, feature analysis, and data pre-processing methods to achieve robust performance. Simulation results shows that the effectiveness of proposed technique through extensive experimentation on datasets, showcasing its ability to accurately forecast app ratings....

Authors: Veena Vadinee Raikwar, Prof. Mahendra Sahare, Prof. Anurag Shrivastava.

2 Jul 01, 2024
ENHANCED PREDICTION OF DIABETES USING MACHINE LEARNING TECHNIQUE

Due to its intricate dependencies on numerous factors, diabetes diagnosis is a very difficult task in the early stages. In order to assist medical experts in the demonstration method, it is necessary to establish restorative symptomatic emotionally supportive networks. Neural system functions have been successfully linked to the diagnosis of many medical conditions. Gradient boosting machine learning is used in this thesis to train the diabetes diagnosis and classify diabetic patients into two groups based on their class values. To attain an accuracy of 81.95% in the suggested strategy, we employed an ensemble of gradient boosting techniques.For diabetic disease dataset, the majority vote-based model, which includes Naïve Bayes, Decision Tree, and Support Vector Machine classifiers, achieved an accuracy of 76.56%, sensitivity of 79.16%, and specificity of 77.476%. ...

Authors: Prachi Patel, Manoj Yadav.

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