New package: EDA v1.0.0

[JuliaRegistrator]

• Registering package: EDA
• Repository: https://github.com/notGiGi/ExploratoryDataAnalysis
• Created by: @notGiGi
• Version: v1.0.0
• Commit: df15d8d61cabab1d94c069aeed49f44470a64f6b
• Reviewed by: @notGiGi
• Reference: https://github.com/notGiGi/ExploratoryDataAnalysis/issues/8
• Description: The EDA Pkg simplifies exploratory data analysis by offering tools for data visualization, cleaning, and preparation. It includes functions for handling missing values, detecting outliers, analyzing correlations, and generating insightful visualizations. Perfect for understanding and preparing datasets for advanced analysis or modeling.
• Release notes:

EDA.jl

## Version 0.1.0

### Highlights
- Initial release of **EDA.jl**, a powerful and comprehensive Julia package for exploratory data analysis.

### Features
#### Data Cleaning and Handling
- `threshold`: Remove columns with missing data exceeding a user-defined percentage.
- `outlierswithiqr`: Detect outliers using the Interquartile Range (IQR) method.
- `outlierhandle`: Handle outliers with configurable options for removal.

#### Data Exploration
- `visualize_data`: Preview the first `n` rows of a dataset.
- `dataType`: Analyze and display data types for all columns.
- `correlation`: Compute correlation matrices for numeric columns.
- `heat`: Generate heatmaps of correlation matrices for easy interpretation.

#### Visualization
- `correlation_network`: Create an interactive graph of correlations between variables.
- Real vs. predicted plots for regression analysis.

#### Statistical Analysis
- `linearregression`: Fit linear regression models with detailed output of coefficients and predictions.

#### Efficiency
- All functionalities are encapsulated in the `EDALoader` structure, allowing for efficient data manipulation with built-in caching.

### Improvements and Fixes
- Optimized correlation and visualization functions to handle missing data gracefully.
- Added error handling and user-friendly messages for all major functions.
- Improved plotting aesthetics for enhanced data visualization.

### Planned Features for Future Releases
- Advanced time-series analysis tools:
  - Seasonal decomposition.
  - Trend detection.
  - Forecasting models (ARIMA, Prophet, etc.).
- Integration with PlotlyJS for interactive visualizations.
- Machine learning integration with Julia ML frameworks.

### Known Issues
- Large datasets may experience performance degradation in some visualization functions. Optimization is planned in upcoming versions.

### How to Upgrade
1. Open Julia's REPL.
2. Run the following commands:
   ```julia
   using Pkg
   Pkg.update("EDA")

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[goerz]

Closing in favor of #122422