This project features a data scraping script developed to extract research articles from the JSTOR database for a meta-analysis on succession planning in the public sector. The script automates the retrieval of publication metadata (such as titles, authors, abstracts, and keywords) to build a structured dataset for systematic review. Designed with efficiency and reproducibility in mind, it supports large-scale literature collection and preprocessing to identify trends, gaps, and empirical findings across studies in public administration research.
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This project implements a machine learning classification algorithm designed to analyze patterns in data and assign observations to predefined categories. It leverages supervised learning techniques, model training, and performance evaluation metrics to achieve accurate and interpretable predictions.
thekylebell/Data-Scraping
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This project implements a machine learning classification algorithm designed to analyze patterns in data and assign observations to predefined categories. It leverages supervised learning techniques, model training, and performance evaluation metrics to achieve accurate and interpretable predictions.
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