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• Monitoring: Continuously track the system's performance, making sure it correctly identifies items and reduces
                   sorting errors.
                    • Maintenance: Regularly update the model with new data to adapt to changes in waste types or introduce new
                   categories as needed.


                             Task                                                  21 st  Century   #Productivity & Accountability
                                                                                       Skills

                   A school wants to predict student absence due to illness, especially during flu season. By predicting the likelihood
                   of  student  absence,  the  school  can  prepare  by  adjusting  lesson  plans,  planning  for  substitute  teachers,  or
                   implementing preventive measures. This project uses AI to help administrators anticipate student's health trends
                   based on data from previous years.
                   Prepare a 4Ws Problem Canvas for the given scenario.





                         Introduction to AI Domains


                 Artificial Intelligence comprises three key domains: Statistical data, Computer Vision, and Natural Language
                 Processing. While each of these domains is unique, together they form the foundation of AI.

                                                              Domains of AI






                                                                                             Natural Language
                              Statistical Data               Computer Vision
                                                                                                Processing

                 Statistical Data


                 Statistical data is a critical domain of Artificial Intelligence (AI) that focuses on the collection, management,
                 analysis, and interpretation of data systems and processes. In this domain, AI systems are designed to collect
                 vast amounts of structured, semi-structured, and unstructured data from various sources, organise and maintain
                 data sets, and apply statistical and machine learning techniques to derive meaningful information from them.

                 The  information  extracted  through  statistical  data  analysis  can  be  utilised  to  identify  patterns,  trends,  and
                 relationships  within  the  data.  This  information  serves  as  a  foundation  for  data-driven  decision-making  that
                 enables organisations and systems to make informed predictions, optimise processes, and solve complex problems.
                 For instance, if I plan to host an outdoor event in August in Delhi, I would need to collect data on weather
                 forecasts for that month, as well as historical weather trends from previous years. This data will serve as the basis
                 for identifying patterns, which can then be used to make predictions about the likely weather conditions on the
                 day of the event. In the world of AI, data is the most important asset.

                 Once data is gathered from various sources, AI systems analyse and process it to extract valuable insights. These
                 insights help AI machines recognise patterns, make informed predictions, and even improve decision-making
                 processes over time. Predictive models, which are built from historical data patterns, can be used in various
                 fields such as weather forecasting, healthcare, finance, and more. Therefore, data is not just the starting point;
                 it’s the very core of the analysis and machine learning process, enabling AI to learn and evolve its capabilities.


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