Heart Attack Risk Analysis 🫀

A data analysis project that explores key risk factors associated with heart attacks using a structured patient dataset. The notebook covers data loading, cleaning, feature engineering, and visual analysis.

📊 Dataset

File: heart_attack_prediction_dataset.csv

Features used in analysis:

Feature	Description
Age	Patient age
Sex	Gender (Male / Female)
Cholesterol	Cholesterol level
Blood Pressure	Systolic/Diastolic (split into two columns)
Heart Rate	Resting heart rate
Diabetes	Diabetic or not (Yes/No)
Family History	Family history of heart disease (Yes/No)
Smoking	Smoking habit (Yes/No)
Obesity	Obese or not (Yes/No)
Alcohol Consumption	Alcohol use (Yes/No)
Exercise Hours Per Week	Weekly exercise hours (rounded up)
Diet	Dietary habits
Previous Heart Problems	Prior cardiac issues (Yes/No)
Stress Level	Categorized as Normal / Moderate / Little High / Extreme
Sedentary Hours Per Day	Daily sedentary hours (rounded up)
Triglycerides	Categorized as Normal / Borderline / High / Very High
Physical Activity Days Per Week	Active days per week
Sleep Hours Per Day	Categorized as Light / Moderate / Healthy
Heart Attack Risk	Target variable (Yes/No)

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🔧 Data Preprocessing Steps

1. Subset selection — Extracted 19 relevant columns from the raw dataset
2. Binary encoding — Converted 0/1 flags to readable Yes/No labels for: Diabetes, Family History, Smoking, Obesity, Alcohol Consumption, Previous Heart Problems, Heart Attack Risk
3. Blood Pressure split — Parsed Systolic/Diastolic format into two separate numeric columns
4. Sleep categorization — Grouped sleep hours into: Light (1–4 hrs), Moderate (5–6 hrs), Healthy (7+ hrs)
5. Triglycerides categorization — Grouped into: Normal (<150), Borderline (150–199), High (200–499), Very High (500+)
6. Stress level categorization — Grouped stress scores (1–10) into: Normal, Moderate, Little High, Extreme
7. Exercise & Sedentary hours — Ceiled to nearest integer for cleaner grouping

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📈 Visualizations (Completed)

• Heart Attack Risk by Gender — Pie chart showing male vs. female distribution among at-risk patients

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🚧 Upcoming Work

• Distribution plots for Age, Cholesterol, Heart Rate
• Risk breakdown by lifestyle factors (Smoking, Obesity, Diet)
• Correlation heatmap

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🛠️ Requirements

python >= 3.8
numpy
pandas
matplotlib

Install dependencies:

pip install numpy pandas matplotlib

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🚀 How to Run

1. Clone the repository or download the files
2. Place heart_attack_prediction_dataset.csv in the same directory as the notebook
3. Open the notebook:

jupyter notebook Heart_Attack_Risk_Analysis.ipynb

4. Run all cells in order (Kernel > Restart & Run All)

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📄 License

This project is for educational and analytical purposes.