yuseow/102_learner_github_tests

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testing for AIAP

active 2024-02-13 → 2024-02-18 (UTC)

Complete coverage27,485 / 27,485 hourly files (100%) · 2 absent upstream2023-08-15 → 2026-10-03 (UTC)

Events

118

Pushes

Pull requests

Issues

Stars

Forks

Activity over time

Daily event counts in the loaded window

Pushes
Pull requests
Issues
Comments
Stars

Top contributors

Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity

Contributor	Contributions	Pushes	PRs	Comments
yuseow	116	1	80	0

Recent activity

Latest issues, pull requests and releases

Pull request#2yuseow2024-02-18 11:46
Issue#26yuseow2024-02-16 05:54
[Sprint 1] Build the data preprocessing and data cleaning steps required based on the EDA results, such as feature engineering and data transformation (e.g. one-hot encoding for categorical data, scaling for nominal data).
Issue#25yuseow2024-02-16 05:54
[Sprint 1] Conduct exploratory data analysis (EDA) to understand the data and identify potential issues (e.g. missing values, outliers, etc.).
Issue#24yuseow2024-02-16 05:54
[Sprint 1] Conduct a literature review on the tabular classification machine learning problem to identify the state-of-the-art models and their potential challenges and limitations.
Issue#23yuseow2024-02-16 05:49
[Sprint 1] Build the data preprocessing and data cleaning steps required based on the EDA results, such as feature engineering and data transformation (e.g. one-hot encoding for categorical data, scaling for nominal data).
Issue#22yuseow2024-02-16 05:49
[Sprint 1] Conduct exploratory data analysis (EDA) to understand the data and identify potential issues (e.g. missing values, outliers, etc.).
Issue#21yuseow2024-02-16 05:49
[Sprint 1] Conduct a literature review on the Tabular Classification machine learning problem to identify the state-of-the-art models and their potential challenges and limitations.
Issue#20yuseow2024-02-16 05:43
[Sprint 1] Build the data preprocessing and data cleaning steps required based on the EDA results, such as feature engineering and data transformation (e.g. one-hot encoding for categorical data, scaling for nominal data).
Issue#19yuseow2024-02-16 05:43
[Sprint 1] Conduct exploratory data analysis (EDA) to understand the data and identify potential issues (e.g. missing values, outliers, etc.).
Issue#18yuseow2024-02-16 05:43
[Sprint 1] Conduct a literature review on the Tabular Classification machine learning problem to identify the state-of-the-art models and their potential challenges and limitations.
Pull request#2yuseow2024-02-15 08:40
Pull request#2yuseow2024-02-15 08:40
Pull request#2yuseow2024-02-15 08:36
Pull request#2yuseow2024-02-15 08:36
Pull request#2yuseow2024-02-15 08:36
Pull request#2yuseow2024-02-15 08:35
Pull request#2yuseow2024-02-15 08:35
Pull request#2yuseow2024-02-15 08:35
Pull request#2yuseow2024-02-15 08:32
Pull request#2yuseow2024-02-15 08:32
Pull request#2yuseow2024-02-15 08:32
Issue#17yuseow2024-02-14 11:06
[Sprint 2] Identify suitable metrics for model evaluation based on the business objective. Evaluate the best model's performance on the test set.
Issue#16yuseow2024-02-14 11:06
[Sprint 2] Conduct cross-validation to evaluate the model's performance. Determine the best model based on the cross-validation results.
Issue#13yuseow2024-02-14 11:06
[Sprint 2] Develop a baseline model to establish a baseline performance for the Tabular Classification machine learning problem.
Issue#15yuseow2024-02-14 11:06
[Sprint 2] Split the data into training and testing sets. Ensure that the training and testing sets are representative of the data. Ensure that test sets are split before data preprocessing to avoid data leakage.

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