Sustainable Development Goal (SDG) 3 aims to ensure healthy lives and promote well-being for all at all ages. It focuses on various aspects of health, aiming to reduce mortality rates, combat diseases, and improve overall health outcomes globally. Genetic disorders can be considered under the Sustainable Development Goal (SDG) 3.4.
Analyzing genomic data manually is challenging and error-prone due to its complexity and volume. Machine learning has transformed many fields by automating analysis processes, including genomics. Machine learning models have been widely used to explore genetic disorders, study genome-wide associations, and improve personalized medicine. However, there are still challenges in accurately analyzing genomic data, especially in the context of genetic disorder prediction.
This project aims to perform Exploratory Data Analysis (EDA) on genomic data focusing on data cleaning and preprocessing stages. The primary objectives are:
To clean and preprocess genomic data to prepare it for further analysis.
To explore basic characteristics and trends in the genomic dataset.
To formulate hypotheses based on the preliminary exploration of the data.
This project contributes to understanding genetic data through Exploratory Data Analysis (EDA) up to the hypothesis formulation stage. The focus is on cleaning and preprocessing genomic data to make it ready for analysis and formulating hypotheses based on initial observations.
