alfonso-wav / eda_5_alfonso_random_forest

Random Forest Practice

In the course of this project, my primary focus is on the initial development of a Random Forest—an ensemble learning technique with notable applications in classification and regression tasks. The data employed for this endeavor has been sourced from a preceding project where I meticulously executed the comprehensive process of dataset cleaning. This involved addressing missing values, handling outliers, and ensuring data integrity. Subsequently, I engaged in exploratory data analysis (EDA), a crucial phase that provided insights into the underlying patterns, trends, and relationships within the dataset.

Moreover, as part of the preparatory stages, I meticulously exported the refined dataset into .csv files. This meticulous preparation is foundational for the subsequent application of the Random Forest algorithm. The results obtained so far exhibit a promising trend towards optimizing accuracy, even though the observed difference, while favorable, may not be characterized as substantial. Nevertheless, the significance lies in the valuable experience gained through the practical implementation of the algorithm and the nuanced understanding of its functioning.

The particular context of this project revolves around addressing a classification problem centered on medical patients. These patients exhibit a spectrum of medical and physical conditions, and the overarching objective is to accurately determine whether they suffer from diabetes or not. This classification task holds significant implications in the realm of healthcare, making the project not only technically intriguing but also socially impactful.

In summary, this undertaking serves as both a practical exploration of the Random Forest algorithm and an applied study in the domain of medical classification. Your attention to this multifaceted effort is sincerely appreciated.

Thank you for reading.