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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 Statistical Data
Artificial Intelligence comprises three key domains: Statistical data, Computer
Vision, and Natural Language Processing. While each of these domains is Domains of AI Computer Vision
unique, together they form the foundation of AI.
Statistical Data Natural Language
Processing
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.
Applications of Statistical Data
Some applications of statistical data are as follows:
• Recommendation system: Recommendation systems use statistical data
to suggest personalised content to users. By analysing user's behaviour,
preferences, and historical interactions, AI models (e.g., collaborative filtering
or content-based filtering) recommend products, movies, or music. Netflix,
Disney + Hotstar and Amazon are prime examples of platforms using such
systems.
• Price Comparison Websites: Price comparison websites are platforms
that allow users to compare prices of products or services offered by
multiple sellers. These websites leverage statistical data to provide
insights, trends, and optimised pricing strategies. Statistical analysis
plays a vital role in organising, interpreting, and presenting the vast
amounts of price-related data collected from various sources.
96 Artificial Intelligence Play (Ver 1.0)-X

