xlsx

Excel data cleaning for MCM/ICM modeling is automated by loading raw files like 'problem_data.xlsx' to handle missing values, detect outliers, and deduplicate rows, outputting a modeling-ready 'processed_data.csv'.

Normalizing indicators for TOPSIS and AHP evaluation models is achieved through built-in preprocessing templates that scale data distributions, ensuring cross-sectional and time-series data meet evaluation model requirements.

Generating lag features and rolling statistics for time-series data in pandas is supported through feature engineering templates that create interaction terms and rolling windows for modeling-ready datasets.

This data preprocessing skill works with standard Python libraries including pandas, numpy, and scikit-learn, providing ready-to-run templates that integrate directly into existing data pipelines.

Validating data quality after Excel preprocessing is done by generating descriptive statistics and simple reports, allowing you to inspect distributions and confirm outlier detection results before modeling.

Limitations when handling raw CSV data for competitive data analysis include reliance on standard Python library capabilities for outlier detection and normalization, requiring properly structured input files without complex merged cells.