I design a multi agent system with 3 agents as an ai assistant and any agent has a role.
Role of agent1= {professional prompt engineer with imposed to new prompt technic.}, role of agent2= {professional data scientist.}, role of agent3= {professional scientific presenter and editor of presentation}. all of 3 agents have a one task that it is {task1}: {task1} = {review this prompt as a {object} for a task that it is {task2} and analysis {object} that it is prompt of {task2} as a prompt structure and tell your mind about this and weakness of this. And suggest ways to improve it. **{phase}={# 1.Data Cleaning Slide

### 1. Structure: "What? So What? Now What?"

- **What: Data Cleaning Phase**
    - This phase, a crucial part of the data preparation stage, focuses on identifying and resolving data quality issues that could negatively impact the accuracy and reliability of a machine learning model. [Our conversation history]
- **So What: Why is it Crucial?**
    - Machine learning algorithms learn patterns from the data they are trained on. If the data is flawed (inaccurate, inconsistent, or incomplete), the model will learn from these flaws, leading to poor and unreliable predictions. [Our conversation history]
- **Now What: Expected Outcome and Next Steps**
    - The data cleaning phase aims to deliver a reliable and consistent dataset ready for feature engineering and model training. The next step is usually feature engineering, where you transform the cleaned data into a format suitable for machine learning algorithms. [Our conversation history]

### 2. Key Activities and Objectives:

- **Key Activities:**
    - **Handling Missing Values:** Addressing missing data points through techniques like deletion (if the data is Missing Completely at Random (MCAR) and the loss is acceptable) or imputation (using mean, median, mode, or more sophisticated methods depending on the nature of missingness - MAR or MNAR). [Our conversation history]
    - **Addressing Outliers:** Investigating and handling outliers based on their cause and potential impact. This might involve removing outliers if they are due to errors, transforming the data to reduce their influence, or choosing algorithms robust to outliers. [Our conversation history]
    - **Correcting Data Inconsistencies:** Standardizing data formats, correcting typos and inconsistencies in data values, and ensuring that data types are compatible with the chosen machine learning algorithms. [Our conversation history]
- **Objectives:**
    - **Improve Data Accuracy:** Eliminating errors and inaccuracies to ensure the data reflects real-world phenomena.
    - **Ensure Data Consistency:** Establishing uniform data representation and eliminating discrepancies to improve data reliability.
    - **Enhance Data Completeness:** Minimizing missing data to provide a comprehensive view of the information, which helps machine learning models learn more effectively.

### 3. Why is Data Cleaning Important?

Data cleaning is essential because it directly impacts the quality of insights derived from machine learning models. A model trained on flawed data will produce flawed predictions, rendering it useless for decision-making. By investing time in data cleaning, you are investing in the accuracy, reliability, and trustworthiness of your machine learning project's outcome.  [Our conversation history]

### 4. Tasks Checked During Data Cleaning:

- **Are there any missing values?** How are they distributed? (MCAR, MAR, MNAR) [Our conversation history]
- **Have outliers been identified and addressed?** What methods were used, and why? [Our conversation history]
- **Are there any inconsistencies in the data?** (formatting, typos, etc.) Have these been resolved? [Our conversation history]

### ~~5. Problem-Solving Approaches (Manual vs. Popular):~~

- **Manual Approach:**
    - This might involve using spreadsheets or text editors to inspect and clean data, often suitable for smaller datasets. However, it can be time-consuming and prone to errors. [Not directly covered in the sources - this is general knowledge]
- **Popular Approaches (Software and Libraries):**
    - Python Libraries: Libraries like Pandas and data manipulation functions in libraries like NumPy and SciPy are commonly used for data cleaning in machine learning projects.
    - Data Science Platforms: Many machine learning platforms and tools provide built-in data cleaning functionalities.

### 6. Scenarios, Strategies, Policies, Decisions:

**So What?** Why Cleaning Matters: Machine learning models are like eager students - they learn from whatever you feed them.  Dirty data leads to inaccurate and unreliable predictions.  Would you trust a recipe with the wrong ingredients?

**Now What?**  What Comes Next: Data cleaning paves the way for **feature engineering**, where we transform data into a delicious format for our machine learning algorithms.

**1. Missing Values - Don't Leave Holes in Your Story:**

- **MCAR Data (Missing Completely at Random):** If a tiny amount of data is missing randomly, deletion *might* be okay.
- **MAR/MNAR Data (Missing at Random/Not at Random):** We need smarter solutions! Imputation methods like using the mean/median/mode or fancier techniques like k-nearest neighbors can fill in the blanks. The choice depends on the data and potential biases. [Our Conversation History]

**2. Outliers - Taming the Data Extremes:**

- **Error Outliers:** If outliers are due to mistakes, removing them is like swatting away pesky flies.
- **Influential Outliers:** Sometimes, we don't want to completely eliminate outliers. Transformation methods (like logarithmic or Box-Cox transformations) can help soften their impact. [Our Conversation History]

**3.  Inconsistencies -  Creating Data Harmony:**

- **Standardizing Formats:** Make sure dates, times, and other values follow the same style guide.
- **Typo Corrections:** Spell-checking and context-based corrections can fix those little data typos.
- **Data Type Alignment:** Ensure all your data types play nicely together for smooth algorithm operation. [Our Conversation History]

### 7. Input and Output of Data Cleaning:

- **Input:** Raw data collected from various sources. [Our conversation history]
- **Output:** A cleaned dataset free from identified errors, inconsistencies, and (to the extent possible) missing values, ready for feature engineering. [Our conversation history]

### 8. Dependencies:

- **Previous Phase:** The data cleaning phase directly depends on the insights and understanding gained during the Data Acquisition & Preparation phase, specifically data exploration. [Our conversation history]
- **Next Phase:** The cleaned dataset from this phase serves as the foundation for the Feature Engineering phase. [Our conversation history]

### 9. Clarity and Conciseness:

Remember to use clear and straightforward language for a broader audience:

- **Instead of "imputation," consider "filling in missing data."**
- **Explain complex terms like "MCAR" and "MNAR" simply.**
- **Visualizations on your slide (e.g., before/after data cleaning) can greatly enhance understanding.**

By following these suggestions, you can create a compelling and informative slide on the data cleaning phase.}}.** {task2} = {Create a presentation slide that slide input is scientific information as a about {phase} and output is a professional slide content and structure. {object}= {As an expert in designing technical presentations and professional data scientist, your task is to optimize the content for a slide in a presentation on "data science project".
The title of this slide is a {phase}. Each slide represents a distinct stage of the machine learning pipeline, from data collection to model deployment. Review and enhance the following slide content, ensuring clarity, scientific rigor, and engagement. Optimize this slide’s content by addressing the following key aspects: 1. Core Message: • Highlight the most critical takeaway related to the {phase} on this slide. • Ensure the core message aligns with the slide’s purpose and contributes to the overall narrative of the machine learning pipeline. • Focus on what the audience needs to understand about this {phase} to move forward in the project. 2. Structure for content of {phase}: 0.Use the "What? So What? Now What?" framework to structure the content for this {object} phase:

What: Clearly explain the current {object} phase in the pipeline.

So, what: Highlight why this {object} phase is crucial in the overall workflow.

Now What: Point out what comes next or the expected results of completing this {object} phase.

1.key activities of {object} phase?

2.objectiveso of {object} phase?
Clearly state the primary goal of this phase.

- Explain how achieving this objective contributes to the overall success of the project

3.brief description: what happens in {object} phase, focusing on key activities and objectives.

4.Why is {object} phase being Important?

- Discuss the significance of this phase within the context of the entire project.
- Highlight any potential impacts on subsequent phases or the final outcome.

5. which task in {object} phase has been checked? question must be answer or what is question as a problem is solved in this {object} phase?

**6.Methods:**

- Outline specific methods or techniques employed during this phase.
- Provide examples where applicable to illustrate how these methods are implemented.

**7.Policies and Decision-Making:**

- **4.1 Decision Criteria**: Describe the criteria used by data scientists to select specific methods and tools for this phase.
- **4.2 Expected Outputs**: Define what outputs are anticipated from this phase and how they will be evaluated.
- **4.3 Tools and Technologies**: List any software, libraries, or frameworks that will be utilized, along with their relevance to achieving the phase's objectives.

**8.Challenges**:

- Highlight major challenges faced in this phase, such as:
    - **Data Quality Issues**: Discuss potential problems with data accuracy or completeness.
    - **Model Overfitting**: Address concerns regarding model performance on training vs. validation datasets.
    - **Integration Difficulties**: Mention challenges in deploying models into production environments.

**9.Structure for Each Phase**:

- **Subsections**: Break down each phase into clear subsections that detail:
    - Key activities involved
    - Tools and technologies used
    - Challenges faced and how they were addressed
    - Expected outputs and deliverables
    
    3. Clarity and Conciseness: • Use clear, jargon-free language, especially for an audience with mixed technical backgrounds. • Limit text to optimum standard slide bullet points, focusing on concise explanations. • Ensure the slide has no more than optimum slide words, excluding titles and references. important rules: Title * Clearly label the slide as {phase} to establish the topic. Introduction * Briefly define {phase} and its importance in the context of data science project. Objectives * State the objective of {phase}. Methods * Outline the techniques used for {phase}. Process * Describe the steps taken during {phase} and also workflow of {phase}. * List all traditional and popular Tools or libraries utilized. Results * Present key findings from the {phase} process: * output of {phase}. * Comparison of model performance before and after selection {phase}. Conclusion * Summarize the impact of {phase} on your overall project outcomes. And expected effect of {phase} in project result. Editor prompt: When creating a slide for the presentation titled "Data Science Project Implementation," consider the following guidelines to enhance clarity and coherence: 1. Avoid Redundant Concepts: Ensure that concepts are not repeated multiple times throughout the slide. Each concept should be explained thoroughly at once, allowing for a smoother flow of information without unnecessary repetition. 2. Content Integrity: Maintain the integrity of the content by presenting each idea clearly and distinctly. This will help prevent redundancy and contribute to a more fluid narrative. 3. Order of Concepts: Structure the slide to transition from general ideas to specific topics effectively. Use transitional sentences to create a seamless connection between different sections, making it easier for the audience to follow along. For example, related sentences about "mid-{phase} components" should be presented after discussing "initial {phase} components" to maintain continuity. 4. Logical Flow: Clearly explain the process of this "{phase}," detailing how inputs lead to outputs. To preserve logical progression, present sections with distinct concepts separately, then illustrate how they connect back to the overarching theme of "data science project implementation." 5. Simplify Complex Concepts: Break down technical jargon into layman’s terms to ensure clarity for all master student of biomedical members. 6. Clear and Concise Language: Use straightforward language to convey complex ideas without overwhelming your audience.}