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    In this data, weight refers to how heavy the fruit is and size describes its shape or form, such as
                     medium, long, round or small. These are called features.
                     The model is trained using this labelled data, where each combination of weight and size is linked
                     to a specific fruit type. By learning from these examples, the model begins to understand patterns
                     between the features and the correct labels.

                     Once trained, if the model is given new data, such as weight 120 grams and size long, it can
                     predict that the fruit is a banana. This shows how labelled data helps the model learn and make
                     accurate predictions.

                             Labelled Data
                                                                              Prediction                   Output

                                                         AI Model                                             Apple
                            Apple  Banana
                                                                                                             Grapes

                           Orange  Grapes                                                                    Banana

                                                                                                             Orange

                                                        Test Data

                     Unsupervised learning
                     Unsupervised learning is a type of machine learning in which machines study data to identify

                     hidden patterns, structures or groupings. In this approach, the model learns from the patterns in
                     data without any predefined labels or outputs.

                     Unlike supervised learning, unsupervised learning does not provide definite guidance or correct
                     answers to the model. Instead, the model independently explores the data to discover meaningful
                     patterns and relationships. It is commonly used for tasks such as clustering similar data points
                     and detecting anomalies in datasets.
                     Some applications of unsupervised learning are as follows:

                       Customer segmentation: Grouping customers based on similar buying habits, preferences or
                        behaviour. It helps businesses target the right audience with suitable products and offers.

                       Market Basket analysis:  Identifying  products  that  are  frequently  bought  together  by
                        customers. This helps stores in product placement and making better recommendations.
                       Document clustering: Grouping similar articles or documents based on their content or topics.
                        It helps in organising large amounts of information efficiently.

                       Anomaly  detection: Detecting  unusual patterns or activities  that  do not match  normal
                        behaviour. It is useful in identifying fraud or suspicious transactions.

                                                                               Introduction to AI & Everyday Examples  23

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