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Problem Scoping
              The beginning of any project requires describing the problem, which
              is done in the problem-defining phase of the project life cycle. It is a
              crucial stage that requires in-depth research of the problem, so that
              the desired solution is well-written in an absolute understandable
              form. This helps progressively in all the following stages of the project
              life cycle,  and  is also used  to  trace  back  any  missed out  features
              that was initially planned to implement in the AI model. Successful
              implementation of the AI model is critically dependent on this stage
              of the AI life cycle.






                                                Data Collection
                                                Once the problem is identified and defined, we then begin to collect the data for
                                                it. The data that needs to be collected is identified and recorded using machines
                                                that record real-world signals for AI model to work successfully. Data acquisition
                                                is followed by data cleansing, that drops the irrelevant information with respect
                                                to the problem at hand. The inputs are then digitised for processing by the AI
                                                model in place.






              Data Exploration
              Data  exploration serves as a fundamental  initial phase in data-driven projects, aimed at  deeply understanding the
              dataset's structure,  characteristics, and  underlying  insights. This critical  process involves summarising essential
              statistics such as data types, distributions, and handling missing values,
              alongside conducting exploratory data analysis (EDA) to unveil patterns,
              relationships, and anomalies. Utilising various visualisation techniques
              such as histograms, scatter plots, and box plots enables analysts to
              visually grasp  data  distributions and  correlations, thereby guiding
              preprocessing decisions like outlier treatment and feature engineering.
              By laying this groundwork, data  exploration  ensures subsequent
              analytical and modeling phases are built upon a robust comprehension
              of the dataset's intricacies,  empowering  data-driven decisions  with
              clarity and reliability.

                                                        AI Modelling
                                                        This is the  phase of implementation  of the  AI model using suitable
                                                        platforms, programming languages, and constructs. An AI model is a
                                                        program that has been trained on a set of data to recognise certain
                                                        patterns or make certain decisions without further human intervention.
                                                        They apply different algorithms to relevant data inputs to achieve the
                                                        tasks, or output, they’ve been programmed for. This model is developed
                                                        to  solve  the  problem  defined  in  the  first  phase.  The  model  is  then
              trained with the training data. The training is iterative in nature, so that the system is prepared for the most unexpected
              scenarios. This produces a refined model ready to be evaluated in the upcoming phase.

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