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--- a/README.md +++ b/README.md @@ -1,35 +1,35 @@ -# Using Machine Learning to Analysis of Blood Donation -## Which features impact high blood donation? - -* Blood donation is the only method to secure accessibility of blood products for patients in need of transfusion and accessibility is solely dependent on voluntary non-remunerated blood donors. -* Many countries' health authorities and blood centers are striving to have sufficient blood donations to secure a stable blood supply in the country or region. -* In this case study, we aimed to find features that could help improve blood donation performance in South Korea. -* The objective of this case study is as follows: - * Analyze the trend of blood donations in South Korea since 2005 - * Identify which features have a significant impact on high blood donation - * Recommendations for blood centers to increase blood donation -* To identify important features, we built various machine-learning models from simple Logistic Regression to the XGBoosting Classifier model. -* Platforms used for this project - * Python: Pandas, Scikit-learn, Xgboost - * Visualization: Tableau, Seaborn -## Project Outline -1. Plan Stage: Data collection, Data dictionary, Pre-processing & Feature engineering -2. Analyze Stage: Exploratory Data Analysis (EDA) -3. Construct Stage: Prepare, Construct, and train & test models -4. Execute Stage: Interpret model performance, conclusion (recommendation) -## Note for this project - * The data used for this project is entirely from public open data, extracted from KOSIS (Korean Statistical Information Service). No internal data was included. - * This project was worked with <ins>**Yoojin (Audrey) Jung**</ins>, a data analyst, with a master's degree from the University of Illinois Urbana-Champaign (UIUC). - * **(Skills)** Data Analytics, Visualization (Tableau), SQL, R, Python - * **(Contributions to this project)** EDA (Processing & Visualization), Tableau, model design (logistic regression, decision tree) - * **(LinkedIn Profile)** https://www.linkedin.com/in/yoojin-jung/ -## Citation -If you use (or refer to) this project in your research (study), please cite it as follows: - * Author: Hyunjin and Yoojin - * Title: Using Machine Learning to Analysis of Blood Donation - * Year: 2024 - * Publisher: Hyunjin-Austin - * Journal: Blood-Donation-Analysis - * How published: https://github.com/Hyunjin-Austin/Blood-Donation-Analysis/ <br><br> - -Thank you for citing our work! +# Using Machine Learning to Analysis of Blood Donation +## Which features impact high blood donation? + +* Blood donation is the only method to secure accessibility of blood products for patients in need of transfusion and accessibility is solely dependent on voluntary non-remunerated blood donors. +* Many countries' health authorities and blood centers are striving to have sufficient blood donations to secure a stable blood supply in the country or region. +* In this case study, we aimed to find features that could help improve blood donation performance in South Korea. +* The objective of this case study is as follows: + * Analyze the trend of blood donations in South Korea since 2005 + * Identify which features have a significant impact on high blood donation + * Recommendations for blood centers to increase blood donation +* To identify important features, we built various machine-learning models from simple Logistic Regression to the XGBoosting Classifier model. +* Platforms used for this project + * Python: Pandas, Scikit-learn, Xgboost + * Visualization: Tableau, Seaborn +## Project Outline +1. Plan Stage: Data collection, Data dictionary, Pre-processing & Feature engineering +2. Analyze Stage: Exploratory Data Analysis (EDA) +3. Construct Stage: Prepare, Construct, and train & test models +4. Execute Stage: Interpret model performance, conclusion (recommendation) +## Note for this project + * The data used for this project is entirely from public open data, extracted from KOSIS (Korean Statistical Information Service). No internal data was included. + * This project was worked with <ins>**Yoojin (Audrey) Jung**</ins>, a data analyst, with a master's degree from the University of Illinois Urbana-Champaign (UIUC). + * **(Skills)** Data Analytics, Visualization (Tableau), SQL, R, Python + * **(Contributions to this project)** EDA (Processing & Visualization), Tableau, model design (logistic regression, decision tree) + * **(LinkedIn Profile)** https://www.linkedin.com/in/yoojin-jung/ +## Citation +If you use (or refer to) this project in your research (study), please cite it as follows: + * Author: Hyunjin and Yoojin + * Title: Using Machine Learning to Analysis of Blood Donation + * Year: 2024 + * Publisher: Hyunjin-Austin + * Journal: Blood-Donation-Analysis + * How published: https://github.com/Hyunjin-Austin/Blood-Donation-Analysis/ <br><br> + +Thank you for citing our work!