Exploratory Data Analysis on Medical Datasets
Disha Patel
Overview
This project involves performing Exploratory Data Analysis (EDA) on a dataset to uncover insights, patterns, and trends. The analysis focuses on understanding the structure and distribution of the data, detecting outliers, and identifying key relationships between variables.
Objective:
To explore and analyze complex medical datasets, clean and prepare them, and derive actionable insights through visualizations.
Key Steps in the EDA:
Data Cleaning: Handling missing values, removing duplicates, and addressing outliers.
Data Transformation: Applying necessary transformations to variables for better analysis.
Descriptive Statistics: Generating summary statistics to understand the central tendency, variability, and distribution of the data.
Data Visualization: Creating visual representations like histograms, box plots, and scatter plots to gain deeper insights.
Tools Used:
Python
Pandas
NumPy
Matplotlib / Seaborn
Key Insights:
Performed Univariate and Bivariate Analysis
Identified correlations between the following attributes of the dataset
Results:
Identification of factors that are responsible for early diabetes.
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Posted Sep 25, 2025
Performed EDA on medical datasets to uncover insights and trends.
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