statistical-distribution-and-outlier-analysis

Outlier detection applies IQR-based algorithms to numerical datasets to identify and report abnormal data points. It processes large datasets with multiple dimensions, providing both visual and quantitative insights for quality control and exploratory data analysis.

Data distribution visualization generates boxplots and histograms to visually assess data spread and identify skewness or kurtosis. It uses seaborn and matplotlib to process numerical data sets without manual preprocessing.

Yes, statistical analysis with pandas and seaborn processes numerical datasets directly. Load your numeric dataset and run the script to produce distribution plots and outlier reports without manual data preprocessing.

Identifying unusual sensor measurements uses IQR-based outlier detection to quickly pinpoint abnormal readings that may indicate sensor faults or environmental anomalies. It generates visual and quantitative reports for data quality assessment.

Yes, statistical distribution analysis supports large datasets with multiple dimensions. It processes numerical data using pandas and numpy, applying IQR algorithms and generating visual reports for data analysts and researchers.