This machine learning project I utilized the SciPy library (scipy, numpy, matplotlib, pandas, and sklearn) to load, summarize, and visualize the dataset. The code prints the dimensions (instances and attributes) of the dataset and displays the first 20 rows of data before providing a summary of each attribute. Additionally, it prints out the class distribution (number of instances for each class).
As for data visualization, the code reveals univariate and multivariate plots to allow users to better understand each attribute, as well as its relationship with others. After testing 6 algorithms (logictic regression, linear discriminant analysis, k-nearest neighbors, classification and regression trees, gaussian naive bayes, and support vector machines), the code selects SVM as the best model, making and evaluating predictions. Ultimately, the code provides a classification report, providing a breakdown of each class based on precision, recall, f1-score, and support.
Courtesy of https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
I merely used the step-by-step guide learn about machine learning, and this introductory project sparked great interest. I am eager to complete more projects to further my knowledge.