testing for AIAP
active 2024-02-13 → 2024-02-18 (UTC)
Complete coverage26,373 / 26,373 hourly files (100%) · 2 absent upstream2023-08-15 → 2026-08-17 (UTC)
Events
118
Pushes
1
Pull requests
80
Issues
35
Stars
0
Forks
0
Activity over time
Daily event counts in the loaded window
Line chart, 6 days from 2024-02-13 to 2024-02-18. Pushes: 1 total, peak 1 in a day. Pull requests: 80 total, peak 39 in a day. Issues: 35 total, peak 26 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.
- 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#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#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#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#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#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#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#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.
- Issue#14yuseow2024-02-14 11:06[Sprint 2] Determine at least 3 suitable models in total for the Tabular, Classification machine learning problem to be trained and evaluated.
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.