yanxin-wang/yanxins-data-science-and-machine-learning-assistant

Missing models (1)

This assistant is using the following blocks which have been deleted:

sambanova/qwen2.5-coder-32b-instruct

Claude 3.7 Sonnet

anthropic

200kinput·8.192koutput

Voyage AI rerank-2

voyage

rerank

voyage-code-3

voyage

embed

OpenAI GPT-4o

OpenAI

128kinput·16.384koutput

DeepSeek R1

deepinfra

DeepSeek R1

sambanova

o1

OpenAI

200kinput·100koutput

OpenAI GPT-4o Mini

OpenAI

128kinput·16.384koutput

qwen2.5-coder 1.5b

ollama

Rules

You are an experienced data scientist who specializes in Python-based
data science and machine learning. You use the following tools:
- Python 3 as the primary programming language
- PyTorch for deep learning and neural networks
- NumPy for numerical computing and array operations
- Pandas for data manipulation and analysis
- Jupyter for interactive development and visualization
- Conda for environment and package management
- Matplotlib for data visualization and plotting

- Follow Django style guide
- Avoid using raw queries
- Prefer the Django REST Framework for API development
- Prefer Celery for background tasks
- Prefer Redis for caching and task queues
- Prefer PostgreSQL for production databases

- Follow the Solidity best practices.
- Use the latest version of Solidity.
- Use OpenZeppelin libraries for common patterns like ERC20 or ERC721.
- Utilize Hardhat for development and testing.
- Employ Chai for contract testing.
- Use Infura for interacting with Ethereum networks.
- Follow AirBnB style guide for code formatting.
- Use CamelCase for naming functions and variables in Solidity.
- Use named exports for JavaScript files related to smart contracts.
- DO NOT TEACH ME HOW TO SET UP THE PROJECT, JUMP STRAIGHT TO WRITING CONTRACTS AND CODE.

Docs

Condahttps://docs.conda.io/en/latest/

Jupyterhttps://docs.jupyter.org/en/latest/

Matplotlibhttps://matplotlib.org/stable/

NumPyhttps://numpy.org/doc/stable/

Pandashttps://pandas.pydata.org/docs/

Pythonhttps://docs.python.org/3/

PyTorchhttps://pytorch.org/docs/stable/index.html

Prompts

Exploratory Data Analysis

Initial data exploration and key insights

Create an exploratory data analysis workflow that includes:

Data Overview:
- Basic statistics (mean, median, std, quartiles)
- Missing values and data types
- Unique value distributions

Visualizations:
- Numerical: histograms, box plots
- Categorical: bar charts, frequency plots
- Relationships: correlation matrices
- Temporal patterns (if applicable)

Quality Assessment:
- Outlier detection
- Data inconsistencies
- Value range validation

Insights & Documentation:
- Key findings summary
- Data quality issues
- Variable relationships
- Next steps recommendations
- Reproducible Jupyter notebook

The user has provided the following information:

Data Pipeline Development

Create robust and scalable data processing pipelines

Generate a data processing pipeline with these requirements:

Input:
- Data loading from multiple sources (CSV, SQL, APIs)
- Input validation and schema checks
- Error logging for data quality issues

Processing:
- Standardized cleaning (missing values, outliers, types)
- Memory-efficient operations for large datasets
- Numerical transformations using NumPy
- Feature engineering and aggregations

Quality & Monitoring:
- Data quality checks at key stages
- Validation visualizations with Matplotlib
- Performance monitoring

Structure:
- Modular, documented code with error handling
- Configuration management
- Reproducible in Jupyter notebooks
- Example usage and tests

The user has provided the following information:

Page

Creates a new Nuxt.js page based on the description provided.

Create a new Nuxt.js page based on the following description.

Client component

Create a client component.

Create a client component with the following functionality. If writing this as a server component is not possible, explain why.

Context

@code

Reference specific functions or classes from throughout your project

@docs

Reference the contents from any documentation site

@diff

Reference all of the changes you've made to your current branch

@terminal

Reference the last command you ran in your IDE's terminal and its output

@problems

Get Problems from the current file

@folder

Uses the same retrieval mechanism as @Codebase, but only on a single folder

@codebase

Reference the most relevant snippets from your codebase

@file

Reference any file in your current workspace

@currentFile

Reference the currently open file

@os

Reference the architecture and platform of your current operating system

Data

Azure Blob Storage

${{ secrets.yanxin-wang/yanxins-data-science-and-machine-learning-assistant/continuedev/azure-blob-storage-dev-data/AZURE_SERVER_URL }}

MCP Servers