heart-disease-analysis

End-to-end Exploratory Data Analysis on a Heart Disease dataset (1,000 patients, 16 clinical & lifestyle features) to identify key risk factors using Python. Cleaned data was also loaded into a PostgreSQL database.

Dataset

• Source: Kaggle
• Shape: 1,000 rows × 16 columns
• Target: Heart Disease (No = 608, Yes = 392)
• Missing values: 340 in Alcohol Intake column (handled via fillna)

Features Analysed

Age, Gender, Cholesterol, Blood Pressure, Heart Rate, Smoking, Alcohol Intake, Exercise Hours, Family History, Diabetes, Obesity, Stress Level, Blood Sugar, Exercise Induced Angina, Chest Pain Type

Key Findings

• Age is the strongest predictor of heart disease (correlation: 0.65)
• Cholesterol shows moderate correlation (0.37)
• Asymptomatic chest pain is most linked to heart disease
• Patients with family history have significantly higher risk
• Less active patients (lower exercise hours) trend toward heart disease
• Stress Level and Blood Pressure showed no strong correlation

Tools Used

Python | Pandas | NumPy | Matplotlib | Seaborn | PostgreSQL | psycopg2 | SQLAlchemy

How to Run

pip install pandas, numpy, matplotlib.pyplot, seaborn, psycopg2, sqlalchemy, jupyter jupyter notebook heart_disease_analysis.ipynb

Files

heart_disease_analysis.ipynb — Main EDA notebook heart_disease_dataset.csv — Raw dataset Cleaned_Heart_Disease.csv — Cleaned output

👤 Author

Bikash Sahani github.com/bikashsahani | linkedin.com/in/bikash-sahani