Portfolio
Orchids: classification using machine learning
This project focused on analyzing and classifying orchid species using data analysis and machine learning techniques. The process involved data preparation, exploratory data analysis, and dimensionality reduction through Principal Component Analysis. Machine learning models, including Logistic Regression, Random Forest Classifier, and K-Means Clustering, were applied to classify known orchid types and predict unknown samples. The analysis revealed distinct clusters corresponding to different orchid species, with high accuracy in classification. The project demonstrated the successful application of various data science techniques to solve a real-world species classification problem, providing valuable insights into distinguishing features of orchid types.
How Does Joint Evolution of Consumer Traits Affect Resource Specialization?
When traits involved in resource acquisition evolve simultaneously, does a consumer that feeds on two resources evolve to become specialized in one, two, or neither of them?
Consequences of life-cycle complexity to the potential for evolutionary diversification
What happens when evolution occurs in traits responsible for resource consumption in a species that undergoes metamorphosis, where each life-stage has its own specific set of resources?
The role of phenotypic plasticity on evolutionary diversification
The ability to shift the phenotype based on the environment s a crucial aspect of the survival of biological organisms. However, there is disagreement about whether it facilitates or hinders diversification. In this study I set out to try to find answers.
Complex life cycles drive community assembly through immigration and adaptive diversification
Life cycle complexity, whereby species have two distinct life stages, can affect community richness in unexpected ways.
Contact me at:
paula (at) paulavasconcelos.net