Experimental Design of Ecological Data Analysis and Machine Learning Based on Python

The Python programming language has become a significant component of bioinformatics experiments due to its widespread application in biological data analysis. Based on a dataset of coral survival environment and distribution, this study constructs an experimental framework integrating Python data analysis and machine learning techniques. The framework aims to explore the effects of environmental factors such as salinity and temperature on coral distribution, and to evaluate the efficacy of models such as random forests and support vector machines in predicting coral presence. Analysis results of the survey data show that corals are mainly distributed in low-latitude regions, and machine learning methods can predict coral presence with reasonable accuracy based on salinity, January temperature, and June temperature. This experimental design provides a reusable technical pathway for ecological data analysis, and its methodological framework can be extended to predict the distribution of other marine organisms and offer support for conservation decision-making.