Machine Learning Portfolio Project

I currently complete another machine learning project here.

Right now, this is still work in progress.

My plan is to fit a simple model to heart failure data from Kaggle and to predict patient survival.

Current Stage: Exploratory Data Analysis (EDA)

Can be found in 02-exploratory_data_analysis.ipynb.

Next Stage: Data Cleaning, Transformation, Feature Engineering

Optimize features for training a model.

Further Stages and Milestones

• Model Training
• Evaluation
• Potentially deployment (depending of if this makes any sense for this project)
• Final documentation (blog post)

Sources

A quick overview of the main sources I used while working on this project.

Data

Raw data is a dataset about heart failure and patient survival from a study by Chicco et al., I acquired from Kaggle.

Davide Chicco, Giuseppe Jurman: Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making 20, 16 (2020)

https://www.kaggle.com/datasets/andrewmvd/heart-failure-clinical-data

Concepts, Techniques and Inspiration

FIXME: Transform this list of bullet points to a proper resource.

• I got a lot of inspiration and knowledge from Aurélien Géron's book Hands-On ML (insert citation here, can be found in book)
• Countless websites for tutorials (cannot all be mentioned, but most relevant ones are mentioned or linked in the notebooks when they are used)
• The Data Camp Podcast with Nick Singh: How to build a DS portfolio

Tools

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• Python
• Pandas
• Scikit-learn
• Kaggle