Asad 20SW042 | Awais 20SW048
Title: Unraveling Infectious Disease Dynamics: A Data-Driven Approach Towards Achieving Good Health and Well-being SDG
In this repository, we present an exhaustive exploration rooted in data science and analytics to specifically address the United Nations' Sustainable Development Goal (SDG) 3, which emphasizes Good Health and Well-being. Recognizing the pivotal role of data in today's digital age, our study dives deep into the spread and dynamics of infectious diseases.
Our primary tools of choice are Python, along with libraries like pandas and Matplotlib, enabling us to process, analyze, and visualize disease trends effectively. Central to our study are key questions concerning gender-based disparities in disease rates, the variations in these rates over distinct periods, and the behavior of the most prevalent diseases in the dataset.
Initial stages involved meticulous data preparation, from loading the dataset to handling categorical variables and missing values. With a primed dataset, our analysis unveiled significant gender disparities, emphasizing the need for tailored health interventions. Furthermore, our temporal analysis has highlighted periods that warrant special attention for potential outbreaks or health concerns.
We also delved into the top 5 diseases by count to gain a detailed understanding of their spread and prevalence over the years. By synthesizing these insights, we present a holistic view of public health trends and their implications.
Beyond the core analysis, our discussion branches out to suggest future avenues for research. We recognize the need for a deeper, more nuanced exploration of gender disparities, potential reasons behind observed temporal fluctuations, and the potential of predictive modeling leveraging advanced machine learning techniques. Moreover, we stress the significance of these insights in crafting evidence-based public health policies and interventions, all with the overarching aim of furthering the objectives of SDG 3.