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omerbasik1
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heart-disease-eda

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age
int64
sex
int64
cp
int64
trestbps
int64
chol
int64
fbs
int64
restecg
int64
thalach
int64
exang
int64
oldpeak
float64
slope
int64
ca
int64
thal
int64
target
int64
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Check out the documentation for more information.

license: mit Heart Disease Prediction Analysis and Preprocessing

Introduction, Data Source, and Project Goal This project presents an Exploratory Data Analysis (EDA) and strategic data preparation for a heart disease prediction dataset. The dataset, sourced from Kaggle, contains over 1,000 patient records with 14 numeric health-related features such as age, cholesterol, blood pressure, and maximum heart rate. The main challenge addressed in this dataset is identifying which medical and demographic factors are most predictive of heart disease. The goal is to prepare and analyze the data for a classification model capable of predicting whether a patient is at risk of heart disease (Class 1) or not (Class 0).

Data Cleaning and Preprocessing Initial cleaning confirmed that the dataset contains no missing or duplicate records, and that all values fall within realistic medical ranges. Outlier detection revealed extreme values in cholesterol and resting blood pressure, but these were retained to preserve real-world clinical variation. Descriptive statistics were calculated to understand the data distribution, and correlation analysis was performed to identify relationships between features and the target variable. The strongest predictors identified were chest pain type (cp), maximum heart rate achieved (thalach), and ST depression (oldpeak). These features showed significant correlation with heart disease presence. Weaker relationships were found in fasting blood sugar (fbs) and cholesterol (chol).

Key EDA Insights and Findings Visual analysis revealed several important patterns relevant to heart disease prediction. Age Pattern: The likelihood of heart disease increases significantly after the age of 45, reflecting higher cardiovascular risk in older populations. Gender Pattern: Men exhibit a higher prevalence of heart disease compared to women. Cholesterol Pattern: Although cholesterol levels are slightly higher among patients with heart disease, the overlap between healthy and affected groups is large, indicating limited standalone predictive power. Heart Rate Pattern: Patients without heart disease tend to achieve higher maximum heart rates, confirming the strong negative relationship between thalach and the target variable. Correlations: The heatmap analysis reinforced that cp, thalach, and oldpeak are the most influential features contributing to prediction strength.

Baseline Model Strategy Following the EDA and preprocessing steps, the dataset is clean and balanced, making it suitable for training classification models. Logistic Regression is recommended as a baseline model, with the focus on achieving high Recall to ensure that as many at-risk patients as possible are correctly identified. Additional strategies could include feature scaling, regularization, and cross-validation to improve model stability and performance.

Video link to my EDA presentation - https://drive.google.com/file/d/1B2H0iHAO2Xjw1qtnAA-Ug8P6X7GlAXic/view?usp=drivesdk

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