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Data Visualisation Technique 5

                  Name of the Representation                                  Bubble Map
                  Description                   It is a combination of a bubble chart, data visualisation and a map. It is used to
                                                visualise location and proportion using circles over geographical regions with
                                                the area of the circle being proportional to its value in the dataset.
                  How to draw?




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                  Suitable for which type of data? It is good for comparing proportions over geographic regions without the issues
                                                caused by regional area size. For example, showing the earthquake-prone areas
                                                in a country, the geographical distribution of whales in the northern hemisphere,
                                                number of tigers in the national parks of India.

                         What is Modelling?


                 Modelling or data modelling is defined as the process of designing decision-making algorithms that have to
                 be trained on a set of data (which was acquired at the data acquisition stage for the problem you scoped in the
                 problem-scoping stage) and applying that learning to recognise certain types of patterns.

                 Once the data is visualised and trends are formed, we need to work with algorithms to prepare the AI model. This
                 can be done by designing our models or using the existing AI models. Before we go into the details of modelling,
                 let us first understand the following important terms:
                    • Artificial Intelligence: AI refers to any technique that enables computers to mimic or imitate, develop, and
                   demonstrate human intelligence. They are machines that can perform tasks that they are programmed for. AI
                   enables machines to think without any human intervention.
                    • Machine Learning: Machines need to learn the ways of humans by learning the techniques and processes. So
                   machine learning is a subset of artificial intelligence that uses statistical methods that enable machines to
                   improve with experiences. So machines learn from their mistakes and take them into consideration in the next
                   iteration,  this  way  they  keep  improving  with  experience.  For  example,  Snapchat  filters  and  Netflix

                   recommendations.                                                                    Artificial
                                                                                                     Intelligence
                    • Deep  Learning: Machines can draw  meaningful inferences from  large
                   volumes of datasets. In deep learning, the machine is trained with a huge           Machine
                                                                                                       Learning
                   amount  of  data,  which  helps it train  itself.  Deep  learning  is a  machine
                   learning algorithm that is  inspired by the functionality of our brain cells
                                                                                                        Deep
                   called neurons. For example,  Google Translate and  image  recognition  in          Learning
                   social media apps.


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