Exploratory Data Analysis on Healthcare Dataset

This project explores the Healthcare and Diabetes dataset to analyze and predict diabetes diagnosis using Exploratory Data Analysis (EDA) and machine learning techniques. The dataset includes medical attributes such as age, glucose levels, BMI, with the target variable being 'Outcome' (diabetic/non-diabetic).

Type: Tabular Data (Binary Classification)

Features: Medical attributes (e.g., age, glucose, BMI) and the target variable (Outcome).

Goal: To perform exploratory data analysis and predict diabetes diagnosis using various machine learning models like Logistic Regression, Decision Trees, and others.

Why I Chose This Dataset

This dataset offers a balanced mix of numerical features, ideal for meaningful EDA and insightful visualizations. It presents a real-world problem statement, predicting the risk of diabetes, making it beginner-friendly yet challenging enough for evaluating models.

Key Insights from My Analysis

Age and BMI emerged as critical factors in identifying diabetes risk.
Glucose levels showed a strong correlation with BMI, indicating lifestyle-related impacts on health.
Outliers in glucose levels point to potential cases requiring further investigation.

Challenges

Interpreting medical metrics, such as insulin levels, required additional domain knowledge for more accurate insights and deeper understanding.

Project Showcase