A clean and reproducible workflow for exploring correlations in datasets using Python.
Ideal for data analysis, visualization, and statistical exploration.

The files inside are aimed at data analysis:

• Distribution characteristics: fat tails, skewness, kurtosis
• Multidimensional correlation: Pearson/Spearman/mutual information
• Missing value pattern
• PCA dimensionality reduction and feature clustering
• Time series-aware EDA methods
• Weighted statistical analysis
• (con't)

─ Data used:

1. data_cp.csv (too big for uploading)
2. data.csv

─ Notebooks:

1. DataVisualization.ipynb
2. FatTail&DriftAwareness.ipynb
3. MissingDataAnalysis.ipynb
4. PCA.ipynb
5. Time-sliced EDA.ipynb
6. CorrelationAnalysis_basic.ipynb