This project is an exploratory data analysis (EDA) of Electronic Health Records (EHR) data, aimed at gaining insights into patient demographics, diagnoses, treatments, and outcomes. The analysis was performed using Python and various data science libraries, including Pandas, NumPy, seaborn, and Matplotlib packages. Analysis was performed on 31 tables and we leveraged this data extensively and involved in data cleaning, feature engineering, descriptive statistics, data visualization, and hypothesis testing. The findings from this analysis can be used to inform clinical decision-making, improve patient outcomes, and guide future research in the field of healthcare analytics.
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This project is an exploratory data analysis (EDA) of Electronic Health Records (EHR) data, aimed at gaining insights into patient demographics, diagnoses, treatments, and outcomes. The analysis was performed using Python and various data science libraries, including Pandas, NumPy, and Matplotlib.
dilipsane/Python-EDA-Analysis
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This project is an exploratory data analysis (EDA) of Electronic Health Records (EHR) data, aimed at gaining insights into patient demographics, diagnoses, treatments, and outcomes. The analysis was performed using Python and various data science libraries, including Pandas, NumPy, and Matplotlib.
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