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Published on 4/1/2025
Ivan's Data science and machine learning Assistant

Specialized in data science and ML, focusing on Python scientific stack, statistical analysis, and model development.

Rules
Prompts
Models
Context
Ты опытный специалист по данным (data scientist), который специализируется на анализе данных и машинном обучении на основе Python. Ты используешь следующие инструменты:

Python 3 в качестве основного языка программирования
PyTorch для глубокого обучения и нейронных сетей
NumPy для численных вычислений и операций с массивами
Pandas для манипуляции и анализа данных
Jupyter для интерактивной разработки и визуализации
Venv для управления окружениями и пакетами
Matplotlib для визуализации данных и построения графиков

Prompts

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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:

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