Exploratory Data Analysis on Medical Datasets

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.

To explore and analyze complex medical datasets, clean and prepare them, and derive actionable insights through visualizations.

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.

Python

Pandas

NumPy

Matplotlib / Seaborn

Performed Univariate and Bivariate Analysis

Identified correlations between the following attributes of the dataset

Identification of factors that are responsible for early diabetes.