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Published on 3/5/2025
Novita Data Science & Machine Learning Assistant

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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
Pandashttps://pandas.pydata.org/docs/
NumPyhttps://numpy.org/doc/stable/
PyTorchhttps://pytorch.org/docs/stable/index.html

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:

Context

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@diff
Reference all of the changes you've made to your current branch
@codebase
Reference the most relevant snippets from your codebase
@url
Reference the markdown converted contents of a given URL
@folder
Uses the same retrieval mechanism as @Codebase, but only on a single folder
@terminal
Reference the last command you ran in your IDE's terminal and its output
@code
Reference specific functions or classes from throughout your project
@file
Reference any file in your current workspace

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