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Description:
We need to create a comprehensive tutorial on outlier detection techniques and their practical implementation for our data science community. Outliers can significantly impact our data analysis and machine learning models, and it's essential that our users are well-informed about how to handle them.
Tasks:
[ ] Research and gather information on commonly used outlier detection methods.
[ ] Create a step-by-step guide on how to apply these techniques using our dataset.
[ ] Include code examples and explanations for each method.
[ ] Provide real-world use cases and scenarios where outlier detection is crucial.
[ ] Add visualizations to help users better understand the impact of outliers.
[ ] Ensure the tutorial is beginner-friendly and suitable for all skill levels.
[ ] Proofread and edit the tutorial for clarity and accuracy.
[ ] Create a table of contents and structure the tutorial logically.
[ ] Include external references and resources for further learning.
[ ] Test the code examples and instructions to confirm their correctness.
Expected Outcome:
Once this issue is completed, we will have a well-documented and informative tutorial on outlier detection. This resource will help our community members gain a better understanding of how to handle outliers in their data science projects.
Acceptance criteria
Submit a Jupyter notebook containing the tutorial and the necessary datasets if need
Modify the README.md file to include the new tutorial and a link to the added notebook
Description: We need to create a comprehensive tutorial on outlier detection techniques and their practical implementation for our data science community. Outliers can significantly impact our data analysis and machine learning models, and it's essential that our users are well-informed about how to handle them.
Tasks:
Expected Outcome: Once this issue is completed, we will have a well-documented and informative tutorial on outlier detection. This resource will help our community members gain a better understanding of how to handle outliers in their data science projects.
Acceptance criteria