You are tasked with generating a structured outline of all phases in the science project, ensuring there is no overlap between phases. You will take input from the user’s study sources to enhance the output. The outline should be step-by-step and divide each phase into sub-phases as needed. For each major phase, break it down into smaller, manageable sub-phases. Identify dependencies between phases, ensuring a clear progression from one phase to the next. The final output should be a detailed and structured outline, with each phase and sub-phase clearly defined.
Example of Current List of Phases:
•  Problem Definition
•  Data Preparation
•	2.1. Data Selection
o	2.1.1. Define the Type of Problem
o	2.1.2. Determine Project Data Requirements
o	2.1.3. Identify Data Sources
o	2.1.4. Evaluate Data Quality
•	2.2. Data Preprocessing
o	2.2.1. Data Cleaning
o	2.2.2. Identify Outliers
o	2.2.3. Remove Duplicate Records
o	2.2.4. Data Integration
•	2.3. Data Transformation
•  Problem-Solving Methods
•  Result Improvement
Instructions:
1.	Review and Refine the Phases:
o	Analyze the current phases and sub-phases for clarity, logical flow, and completeness.
o	Suggest improvements or reorganizations to enhance the overall structure and effectiveness of the outline.
2.	Detailed Sub-Phase Breakdown:
o	For each major phase, ensure that there are clearly defined sub-phases that represent distinct steps.
o	Each sub-phase should have a brief description detailing its objectives and key activities.
3.	Dependencies:
o	Identify and document dependencies between different phases and sub-phases to clarify the progression of tasks.
o	Ensure that each phase logically leads to the next in the context of a data science pipeline.
4.	Output Requirements:
o	The final output must be well-organized and clearly formatted for documentation or presentation.
o	Make sure to maintain a focus on data science best practices throughout the outline.


study source:{
https://jakevdp.github.io/PythonDataScienceHandbook/

https://scikit-learn.org/stable/

https://bloomberg.github.io/foml/#home

https://hsf-training.github.io/hsf-training-ml-webpage/

https://microsoft.github.io/Data-Science-For-Beginners/#/

https://github.com/masaok/machine-learning-notes

https://github.com/josephmisiti/awesome-machine-learning

https://d2l.ai/chapter_preliminaries/ndarray.html

https://developers.google.com/machine-learning/crash-course

https://www.coursera.org/learn/machine-learning

https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/

https://www.microsoft.com/en-us/research/publication/pattern-recognition-and-machine-learning/

https://www.deeplearningbook.org/

https://www.deeplearning.ai/machine-learning-yearning/

https://towardsdatascience.com/a-comprehensive-guide-to-machine-learning-44986f4ec768

https://www.manning.com/books/the-hundred-page-machine-learning-book

https://www.oreilly.com/library/view/practical-statistics-for/9781492072875/

https://www.datasciencehandbook.org/

http://www.cs.ubc.ca/~murphyk/MLbook/

https://www.statlearning.com/

https://web.stanford.edu/~hastie/ElemStatLearn/

https://course.fast.ai/

https://www.coursera.org/specializations/deep-learning

https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2005/

https://scikit-learn.org/stable/documentation.html

https://www.kaggle.com/learn

http://cs229.stanford.edu/
https://www.scaler.com/courses/machine-learning-course-training/?utm_source=organic_blog&utm_medium=in_content_footer&utm_content=step-by-step-guide-to-write-machine-learning-algorithm

https://www.scaler.com/blog/machine-learning-pipelines/
https://github.com/mrdbourke/machine-learning-roadmap?tab=readme-ov-file

https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
}