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Give Life: Predict Blood Donations

Build a binary classifier to predict if a blood donor is likely to donate again.

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  • 11 tasks
  • 614 participants
  • 1,500 XP

Project Description

> "Blood is the most precious gift that anyone can give to another person — the gift of life." ~ World Health Organization

Forecasting blood supply is a serious and recurrent problem for blood collection managers: in January 2019, "Nationwide, the Red Cross saw 27,000 fewer blood donations over the holidays than they see at other times of the year." Machine learning can be used to learn the patterns in the data to help to predict future blood donations and therefore save more lives.

In this Project, you will work with data collected from the donor database of Blood Transfusion Service Center in Hsin-Chu City in Taiwan. The center passes its blood transfusion service bus to one university in Hsin-Chu City to gather blood donated about every three months. The dataset, obtained from the UCI Machine Learning Repository, consists of a random sample of 748 donors. Your task will be to predict if a blood donor will donate within a given time window. You will look at the full model-building process: from inspecting the dataset to using the tpot library to automate your Machine Learning pipeline.

To complete this Project, you need to know some Python, pandas, and logistic regression. We recommend one is familiar with the content in DataCamp's Manipulating DataFrames with pandas, Preprocessing for Machine Learning in Python, and Foundations of Predictive Analytics in Python (Part 1) courses.

Project Tasks

  • 1Inspecting transfusion.data file
  • 2Loading the blood donations data
  • 3Inspecting transfusion DataFrame
  • 4Creating target column
  • 5Checking target incidence
  • 6Splitting transfusion into train and test datasets
  • 7Selecting model using TPOT
  • 8Checking the variance
  • 9Log normalization
  • 10Training the linear regression model
  • 11Conclusion
Instructor Avatar
Dimitri Denisjonok

Python Backend Developer at Futrli

After graduating with a degree in Mathematics from the University of Nottingham (UK), Dimitri decided to pursue software engineering in Python. He has worked on various data related projects: from data visualizations to building data pipelines and machine learning models. His main interest is in Applied Data Science and how businesses and individuals can become more data-driven in their decision making.

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Technology

  • Python LogoPython
  • Topics

    Data ManipulationMachine LearningImporting & Cleaning Data