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README.md

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-license: mit
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+---
+license: mit
+---
+# ❤️ Heart Disease Dataset (Enhanced with Feature Engineering)
+
+## 📌 Overview
+
+This dataset is an **enhanced version of the classic UCI Heart Disease dataset**, enriched with extensive **feature engineering** to support advanced data analysis and machine learning applications.
+
+In addition to the original clinical features, several **derived variables** have been introduced to provide deeper insights into cardiovascular risk patterns. These engineered features allow for improved predictive modeling and more robust exploratory data analysis.
+
+---
+
+## 📊 Original Features
+
+| Column | Description |
+|--------|-------------|
+| `age` | Age of the patient (years) |
+| `sex` | Gender (1 = male, 0 = female) |
+| `cp` | Chest pain type (1 = typical angina, 2 = atypical angina, 3 = non-anginal pain, 4 = asymptomatic) |
+| `trestbps` | Resting blood pressure (mm Hg) |
+| `chol` | Serum cholesterol (mg/dl) |
+| `fbs` | Fasting blood sugar > 120 mg/dl (1 = true, 0 = false) |
+| `restecg` | Resting electrocardiographic results (0 = normal, 1 = ST-T abnormality, 2 = left ventricular hypertrophy) |
+| `thalach` | Maximum heart rate achieved |
+| `exang` | Exercise-induced angina (1 = yes, 0 = no) |
+| `oldpeak` | ST depression induced by exercise |
+| `slope` | Slope of the peak exercise ST segment (1 = upsloping, 2 = flat, 3 = downsloping) |
+| `ca` | Number of major vessels colored by fluoroscopy |
+| `thal` | Thalassemia (3 = normal, 6 = fixed defect, 7 = reversible defect) |
+| `num` | Diagnosis of heart disease (0 = no disease, 1-4 = disease severity levels) |
+
+---
+
+## 🧠 Engineered Features
+
+| Feature | Description |
+|--------|-------------|
+| `age_group` | Age category (`30s`, `40s`, `50s`, `60s`) |
+| `cholesterol_level` | Cholesterol status (`low`, `normal`, `high`) |
+| `bp_level` | Blood pressure status (`low`, `normal`, `high`) |
+| `risk_score` | Composite risk score: (`age * chol / 1000 + trestbps / 100`) |
+| `symptom_severity` | Severity index based on symptoms: (`cp * oldpeak`) |
+| `log_chol` | Logarithm of cholesterol level |
+| `log_trestbps` | Logarithm of resting blood pressure |
+| `age_squared` | Square of patient age |
+| `chol_squared` | Square of cholesterol level |
+| `age_thalach_ratio` | Ratio of max heart rate to age (plus 1) |
+| `risk_factor` | Advanced risk factor: (`cp * oldpeak * thal`) |
+| `missing_values` | Count of missing values in `ca` and `thal` |
+| `chol_trestbps_ratio` | Ratio of cholesterol to resting BP |
+| `log_thalach_chol` | Log of (heart rate × cholesterol) |
+| `symptom_zscore` | Z-score of symptom severity |
+| `avg_chol_by_age_group` | Average cholesterol for corresponding age group |
+| `thalach_chol_diff` | Difference between max heart rate and cholesterol |
+| `symptom_severity_diff` | Deviation from average symptom severity by age group |
+| `age_chol_effect` | Product of age and cholesterol |
+| `thalach_risk_effect` | Product of max heart rate and risk score |
+| `age_trestbps_effect` | Product of age and resting BP |
+| `chol_risk_ratio` | Ratio of cholesterol to risk score |
+
+---
+
+## 💡 Use Cases
+
+- Cardiovascular risk prediction using machine learning
+- Exploratory data analysis for medical research
+- Feature selection and dimensionality reduction tutorials
+- Educational purposes in data science and healthcare analytics
+- Model interpretability studies using engineered variables
+
+---
+
+## 📁 File Info
+
+- **Format**: CSV or DataFrame (depending on hosting)
+- **Instances**: ~300 patients
+- **License**: MIT (Open Source)
+- **Source**: Based on UCI Heart Disease Dataset + custom engineered features
+
+---
+
+## 📬 Citation & Contact
+
+If you use this dataset in your research or application, please consider citing the original UCI dataset and giving credit to this enhanced version's author.
+
+**Author**: [nezahatkk](https://huggingface.co/nezahatkk)  
+**GitHub**: [https://github.com/nezahatkk](https://github.com/nezahatkk)
+
+---
+