Introduction

Source code and data for open source data science for social good. This is a data science portfolio.

List of projects

1. university_sexcrimes

   Analysis of data on sex crimes in US university campuses.

2. heart_disease_risk_prediction

   Predicting heart disease risk from open data.

3. cancer_mortality_prediction

   Predicting cancer survival using logistic regression from open data.

4. predicting_news_popularity

   Predicting popularity of news articles from open data.

5. opensource_mapping_project

   Open source mapping project.

6. astroinformatics

   Analysis of astronomy data using machine learning techniques.

7. scientific_collaboration

   Project to analyze planetary scale scientific collaboration data.

8. accident_prediction

   Road accident forecasting and data exploration project.

   Interactive website using shiny at:

   https://neelsoumya.shinyapps.io/accident_prediction/

9. patterns_in_crime

   Predicting patterns of crime using data science. Larger cities have disproportionately more crime per capita compared to smaller cities (super-linear scaling of crime). We used techniques from dynamical systems and complex systems to explain the super-linear scaling of crime in cities and other socio-technological systems

10. spam_classification

    Building an SVM based spam classifier trained on data from the UCI repository

11. breast_cancer_prediction

    Downloads data from the UCI machine learning repository to make predictions for breast cancer. A few features turn out to be really important for prediction like epithelial cell size. This uses a random forest.

12. funding_trends_science

    Project to analyze data on funding trends in biomedical science.

13. infectious_disease_prediction

    Project to analyze data on emerging infectious diseases.

14. forecasting_imports

    Project to forecast imports and model supply chains.

15. deep_learning_basic

    Basic deep learning model using keras for prediction.

16. ai_healthcare

    Machine learning and AI applied to healthcare.

17. ai_social_good

    Machine learning, data science and AI for social good.

18. ai_bigdata_biology

    Machine learning and bioinformatics for big data in biology.

19. browser_based_data_science

    Browser based data science for democratic access to data science tools.

20. clinical_informatics

    Open source privacy-preserving clinical informatics.

21. policy_paper_general_public

    Policy paper for general public on Ethical Artificial Intelligence (EAI) for social good.

22. nlp

    Resources, code and data for natural language processing.

23. self_organising_map_wine_dataset

    A self organising map (SOM) on the UCI wine dataset using the Orange data science tool.

24. outreach

    Outreach for machine learning and AI for general public

25. teaching_resources

    Teaching resources for machine learning, data science and AI for a general audience

What is this repository for?

• Quick summary

  • Open source code and data for open source data science.

Citation

• If you use this code, please cite the paper and code

  • Citizen Data Science for Social Good: Case Studies and Vignettes from Recent Projects https://doi.org/10.13140/RG.2.1.1846.6002

  • Citizen Data Science for Social Good in Complex Systems, Interdisciplinary Description of Complex Systems, 16(1):88-91, 2018 http://indecs.eu/index.php?s=x&y=2018&p=88-91

  • Banerjee, Soumya. (2017, September 3). Citizen Data Science for Social Good: Case Studies and Vignettes from Recent Projects (Supplementary Resources). Zenodo. http://doi.org/10.5281/zenodo.883783

  ![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.883783.svg)](https://doi.org/10.5281/zenodo.883783)
  

• These projects are an example of my approach to data science for good. I work very closely with domain experts and stakeholders and use computational tools for good. I outline my design and work philosophy below.

  • 

Installation

Install R, R Studio, MATLAB and Python

Install R

https://www.r-project.org/

and R Studio

https://www.rstudio.com/products/rstudio/download/preview/

source("https://raw.githubusercontent.com/neelsoumya/rlib/master/INSTALL_MANY_MODULES.R")

Install Python dependencies as follows:

    pip3 install -r requirements.txt   

Contact

 Soumya Banerjee
 
 https://sites.google.com/site/neelsoumya/
 
 sb2333@cam.ac.uk