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Machine Learning-Based Mobile Applications Using Python and Scikit-Learn

Machine Learning-Based Mobile Applications Using Python and Scikit-Learn
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Author(s): Santhosh Kumar Rajamani (Maharashtra Institute of Medical Education and Research, India & Dr. Bhausaheb Sardesai Talegoan Rural Hospital, India)and Radha Srinivasan Iyer (SEC Centre for Independent Living, India)
Copyright: 2023
Pages: 25
Source title: Designing and Developing Innovative Mobile Applications
Source Author(s)/Editor(s): Debabrata Samanta (Rochester Institute of Technology, Kosovo)
DOI: 10.4018/978-1-6684-8582-8.ch016

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Abstract

This chapter gives a broad outline of machine learning on Android mobile phones using the Scikit-learn module. The first section introduces the reader to Python language; next, Python on Android is introduced with a brief historical note on implementations of Python on Android mobile phones. Pydroid3 is introduced in the subsequent section. This is followed by instructions on setting up an Android phone for machine learning. This is followed by a description of supportive modules for machine learning that are available for Pydroid3, and some example codes, namely: os, pathlib, Pandas, NumPy, SciPy, Matplotlib, Seaborn, PySimpleGUI, NetworkX, Biopython, WordCloud, Kivy, and Jupyter Notebook. The last section of this compilation describes the Scikit-learn library, basic concepts of the Scikit-learn module, and algorithms available with this module, namely: Linear Regression, Logistic Regression, Principal Component Analysis (PCA), XGBoost, K-nearest neighbors, and support vector machine.

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