gu-dsan6725 When performing exploratory data analysis, follow these steps:
- Load the data into a polars DataFrame. Identify the target variable and feature columns.
- Compute summary statistics including mean, median, std, min, max for each numeric feature
- Check for missing values and report the count and percentage per column
- Check for duplicate rows and report how many exist
- Generate distribution plots for each numeric feature using matplotlib histograms
- Create a correlation matrix heatmap using seaborn
- Identify outliers using the IQR method and log the count per feature
- Log a summary of key findings using the project's logging format
- Save all plots to the
output/directory
Use polars (not pandas) for all data manipulation. Follow the coding standards in CLAUDE.md.
If $ARGUMENTS specifies a dataset or file path, use that. Otherwise, ask the user what data to analyze.