Heart Failure Prediction Project

A single Jupyter Notebook that builds and evaluates models to predict heart failure outcomes. All work was performed on Kaggle; no local data, code, or environment is required.

Files

• Notebook: HeartFailurePredictionProject.ipynb — main analysis and model pipeline.

Dataset

• Source: Dataset used and referenced inside the notebook was loaded via Kaggle (added through the notebook's "Add data" UI). No local files were used or required.

How to run (Kaggle only)

• Step 1: Go to Kaggle and create a new Notebook (or open a new Kernel).
• Step 2: Upload the HeartFailurePredictionProject.ipynb notebook to your Kaggle notebook (or import it via the Notebook upload feature).
• Step 3: In the Kaggle notebook, click Add data and attach the same Kaggle dataset used in the notebook. (The notebook expects the dataset to be available in the Kaggle environment.)
• Step 4: (Optional) In the Notebook settings, select a GPU/TPU if you plan to run heavy models — otherwise the default CPU environment is sufficient.
• Step 5: Run the notebook cells top-to-bottom.

Dependencies

Uses typical Kaggle Python stack: pandas, numpy, scikit-learn, matplotlib, seaborn (Kaggle already provides these). If you add extra packages in the notebook, install them via %pip install inside the Kaggle notebook.

Notes

This repository intentionally uses only Kaggle for data and execution — there are no local scripts, environment files, or local data downloads.