outlier-detection-and-quality-assessment

To assess data quality and detect outliers in Excel, load your dataset into the script which uses the IQR method on numerical columns and generates boxplots to visually flag anomalies for cleaning workflows.

The IQR method identifies anomalies in numerical data by calculating the interquartile range to flag potential errors or fraud, ensuring accurate reporting across financial, operational, and scientific datasets.

Analyze data distribution and skewness in Python by running evaluations that calculate skewness and kurtosis using pandas and numpy, providing insights into data shape characteristics to inform preprocessing steps.

Yes, you can visualize data spread and outliers using pandas and seaborn. The script generates boxplots with matplotlib and seaborn to visually identify data distribution and support data cleaning efforts.

Yes, this outlier detection approach works for financial and operational data analysis. It detects abnormal figures, evaluates distribution quality, and improves data integrity for downstream analysis.

A limitation of using IQR for outlier detection is that it primarily evaluates numerical data based on distribution features like skewness and kurtosis, meaning it may require additional preprocessing for datasets with complex non-numerical anomalies.