Outlier and skewness analysis for all features

Create histogram and boxplot views for all input features.
Compute IQR-based outlier counts per feature and identify top 3 features with strongest skew.
Write 4 practical preprocessing recommendations.

No specific file, test, or entry point is named; begin by locating the repository's existing Jupyter Notebook analysis entry point and reviewing how input features are loaded. The work is complete when every input feature has histogram and boxplot views, IQR-based outlier counts, the three most skewed features are identified, and four practical preprocessing recommendations are written.

Written by the indexing model from the issue text.