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    Without proper data acquisition, even the most advanced algorithms will produce poor results.
                     Hence, this stage ensures the AI model has a reliable and representative dataset that reflects
                     real-world conditions.

                     Following are the points that shows why is data acquisition important:

                        It provides the raw material for building AI models.
                        Helps in identifying patterns, trends and relationships.

                        Influences the accuracy, fairness and efficiency of AI outcomes.
                        Aids in detecting biases and making the system more inclusive.

                     Types of Data Collected

                     The success of any AI system depends heavily on the quality and variety of data collected during
                     the Data Acquisition stage. Different problems require different types of data and each data type
                     brings unique value and challenges.

                                                                Types of Data

                                            Textual        Numerical         Visual          Audio
                                             Data            Data            Data             Data

                     Textual Data

                     Textual data consists of information presented in written or printed form—typically as letters,
                     words, sentences or paragraphs.

                                                                                     Stages of AI Project Cycle
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