Initiation project JEST 2020/1

This repository contains the initiation project developed to make my entry as a member of the JEST 2020/1 IT team.

The project has two parts, an introductory (part 1), optional, and another mandatory (part 2).

Environment

Use Python 3.7 or higher.

Install the required packages with pip install -r requirements.txt

Projects

├── 1) Predict Pulsar star [2-5] # Classify stars between Pulsar and non-Pulsar
├── 2) Leukemia Detect [1, 4, 5]  # Classify patients between leukemia or not  

Project 1 [2, 3, 4, 5] overview:

This is a classification problem. You need to classify a star dataset in Pulsar and non-Pulsar. So, the sample data has a total of 17,898 entries (rows) and 8 features (columns).

Note: The dataset is unbalanced (non-Pulsar: 16259; Pulsar: 1639).

Project 2 [1, 4, 5] overview:

This is also a classification problem. You need to classify a patient dataset in with leukemia (1) or not (0). Therefore, the sample data has a total of 178 entries (rows) and 186 features (columns).

Note 1: Only the first 128 entries are labeled, I will use this slice for train and test. The left 50 unlabeled will be used to predict.
Note 2: Again, the dataset is unbalanced (0: 111 patients; 1: 17 patients).
Note 3: Feature selection techniques were applied.

Links

Project 1: Predict Pulsar
Project 2: Leukemia Detect - Disabled

Citation

Tamagusko, T. (2020). Initiation Project JEST 2020/1. Retrieved from https://github.com/tamagusko/jest20201

@misc{TamaguskoJest20201,
  author = {Tamagusko, Tiago},
  title = {Initiation Project JEST 2020/1},
  year = {2020},
  url = {https://github.com/tamagusko/jest20201}
}

References

[1] Dataset to support the study. (2020, May 31). Retrieved from https://github.com/spingegod/ProjetoTI_part2
[2] Dataset to support the study. (2020, Jun 03). Retrieved from https://github.com/spingegod/ProjetoTI_part1
[3] Kaggle (2021). Predicting a Pulsar Star (2021, Apr 15). Retrieved from https://www.kaggle.com/colearninglounge/predicting-pulsar-starintermediate
[4] van Rossum, G. (1995). Python tutorial, May 1995. WI Report CS-R9526, CS-R9526, 1–65.
[5] Breiman, L. (2001). Random forests. 28. https://doi.org/http://dx.doi.org/10.1023/A:1010933404324

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Please direct bug reports and pull requests to the GitHub page. To contact me directly, send email to [email protected].

-- Tiago

License

CC-BY-NC-ND-4.0 (c) 2020, Tiago Tamagusko.