DataInsightFramework is a versatile and scalable data analysis project designed to adapt to various domains, ranging from e-commerce and healthcare to finance and travel. Its core purpose is to provide a comprehensive toolkit for extracting meaningful insights from large datasets, utilizing advanced data processing, feature analysis, and predictive modeling techniques.
- Domain-Agnostic Data Processing: Robust preprocessing methods adaptable to different data types.
- Dynamic Feature Selection: Implements multiple feature ranking methods, including Recursive Feature Elimination (RFE), Stability Selection, and Random Forest feature importance, tailored to diverse datasets.
- Versatile Predictive Modeling: Employs a range of statistical and machine learning models to suit various analytical requirements.
- Customizable Visualization Tools: Provides tools for creating insightful visual representations of data and analysis results.
Clone the repository to get started with DataInsightFramework:
git clone https://github.com/your-username/Feature-Analysis-for-Classification.gitEnsure these are installed:
- Python 3.x
- Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Statsmodels
Install the required packages:
pip install pandas numpy matplotlib seaborn scikit-learn statsmodels- Data Setup: Load and preprocess data from your specific domain.
- Feature Analysis: Utilize various techniques to select and rank features.
- Model Development: Construct and evaluate models based on the dataset characteristics.
analysis_script.py: Core script containing data processing, feature analysis, and modeling components.data/: Directory for datasets. Replace placeholder paths with actual data paths.visuals/: Directory for generated plots and visualizations.