This script illustrates a comprehensive approach to evaluating hydrological data (here, EgaEnEstellaQts, a series of streamflow measurements on the Ega River at the Estella station in Spain) through different methods. First, it applies a ROC (Receiver Operating Characteristic) analysis to test the discriminative capacity of the predictions. It then implements performance measures (goodness-of-fit) to compare observations and simulations, supported by graphical visualizations such as adjustment curves, scatter plots, and time series. Finally, the code provides descriptive statistics and an analysis of the data distribution. The purpose of this script is to combine statistical and graphical tools in order to assess the quality of the simulations, detect potential biases, and provide a comprehensive view of model performance when applied to environmental or hydrological datasets.

Customer spending behavior plays a crucial role in shaping retail strategies, with the spending score serving as a valuable indicator for segmenting customers and personalizing marketing campaigns

Milestone report: cleaning/sampling SwiftKey English data, tokenization + profanity filter, EDA of 1–3-grams with coverage, and a compact n-gram backoff model for next-word prediction.