jonas-72/-Classification-of-Legislation-in-Zambia-using-Machine-Learning

Classification of legislation in Zambia; given a bill/act/Statutory Instrument, classify the type of legislation using machine learning dataset is created from the information on zambialii.org # -Classification-of-Legislation-in-Zambia-using-Machine-Learning Classification of legislation in Zambia; given a bill/act/Statutory Instrument, classify the type of legislation using machine learning dataset is created from the information on zambialii.org **************************************************************************************************************************************************************** In Zambia, there are three types of legislation: bills, acts, and statutory instruments. Bills are proposed laws that have not yet been passed by Parliament. Acts are laws that have been passed by Parliament and have received the President's signature. Statutory instruments are laws that are made by a Minister or other delegated authority under the authority of an Act of Parliament. To classify a specific piece of legislation, it is important to first determine whether it is a bill, an act, or a statutory instrument. If it is a bill, it has not yet been passed by Parliament and is still being debated and revised. If it is an act, it has already been passed by Parliament and signed by the President. If it is a statutory instrument, it has been created by a Minister or other delegated authority under the authority of an Act of Parliament. Once the type of legislation has been determined, it is also important to consider its content and purpose in order to fully understand its impact and significance. 1. Data Collection: First, you need to collect the data from the website. You can use web scraping techniques to extract the necessary information from the website. Python libraries like BeautifulSoup, Scrapy, or Selenium can be helpful in this process. 2. Data Preprocessing: After collecting the data, you need to preprocess it. This can include cleaning the data, removing any irrelevant or duplicate information, and structuring the data in a suitable format for analysis. This step ensures that the dataset is ready for machine learni …