Data Visualization and Preparation

Create basic plots (line, scatter, histogram) using Matplotlib

Create common statistical plots (e.g., box plots, heatmaps) using Seaborn to explore data

Apply feature scaling techniques (StandardScaler, MinMaxScaler)

Encode categorical variables using one-hot and label encoding

Handle skewed data distributions using transformations (e.g., log transform)

Create new features from existing data (e.g., interaction terms)

Build a data preprocessing pipeline using scikit-learn's Pipeline and ColumnTransformer