The dataset includes 1,280,918 speech fragments of Greek parliament members in debate order exported from 5,355 parliamentary sitting record files, with a total volume of 2.12 GB. The speeches extend chronologically from July 1989 up to July 2020. Table 1 shows the contents of the dataset. The names of the speakers are provided in the format "last_name patronym first_name (nickname)". In cases with more than one first or last names, the names that belong to the same category (first or last) are connected with a dash, e.g., "merk-ouri stamatiou amalia-maria (melina)". A parliamentary period is defined as the time span between one general election and the next. A parliamentary period includes multiple parliamentary sessions. A session is a time span of usually 10 months within a parliamentary period during which the parliament can convene and function as stipulated by the constitution.

Keeping track of how lawmakers vote is essential for government transparency. While many parliamentary voting records are available online, they are often difficult to interpret, making it challenging to understand legislative behavior across parliaments and predict voting outcomes. Accurate prediction of votes has several potential benefits, from simplifying parliamentary work by filtering out bills with a low chance of passing to refining proposed legislation to increase its likelihood of approval. In this study, we leverage advanced machine learning and data analysis techniques to develop a comprehensive framework for predicting parliamentary voting outcomes across multiple legislatures. We introduce the Voting Prediction Framework (VPF) - a data-driven framework designed to forecast parliamentary voting outcomes at the individual legislator level and for entire bills. VPF consists of three key components: (1) Data Collection - gathering parliamentary voting records from multiple countries using APIs, web crawlers, and structured databases; (2) Parsing and Feature Integration - processing and enriching the data with meaningful features, such as legislator seniority, and content-based characteristics of a given bill; and (3) Prediction Models - using machine learning to forecast how each parliament member will vote and whether a bill is likely to pass. The framework will be open source, enabling anyone to use or modify the framework. To evaluate VPF, we analyzed over 5 million voting records from five countries - Canada, Israel, Tunisia, the United Kingdom and the USA. Our results show that VPF achieves up to 85% precision in predicting individual votes and up to 84% accuracy in predicting overall bill outcomes. These findings highlight VPF's potential as a valuable tool for political analysis, policy research, and enhancing public access to legislative decision-making.