Page 169 - Robotics and AI class 10
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• The ‘+’ and ‘-’ signs are indicators of the nature of a relationship. The arrowhead depicts the direction of the
effect and the sign (+ or -) shows their relationship.
• If the arrow goes from X to Y with a + sign, it means that both are directly related to each other. That means if
X increases, Y also increases and vice versa.
• If the arrow goes from X to Y with a -sign, it means that both the elements are inversely related to each other.
That means if X increases, Y would decrease and vice versa.
After collecting accurate data, the next step is data exploration. Data exploration means to find the patterns and
trends in the data. It is the third stage in the AI project cycle and the initial step in data analysis. It is used to
understand what is in a dataset and the characteristics of the data.
Data exploration cleans the big data to provide an input to an AI project. Terabytes of data sitting in the data centre
unused is a burden, if correctly processed it can become digital gold.
Importance of Acquiring Relevant Data from Reliable Sources
Acquiring relevant data from reliable sources is vital in AI as it ensures accurate and trustworthy model development.
• Reliable data enhances the system's performance and generalisation capabilities.
• It provides a solid foundation for making informed decisions and generating valuable insights.
• Access to reliable data fosters ethical AI practices and promotes user trust.
Ultimately, acquiring relevant data from reliable sources is necessary for achieving reliable and impactful AI solutions.
Sources of Dataset in AI – Kaggle Platform
Kaggle is a web platform, which is a subsidiary of Google. The platform allows enthusiasts to find datasets they
wish to use in building their AI models and publish datasets. It also gives them the opportunity to work with
other data scientists and machine learning engineers. The Kaggle platform gives users the options of entering
competitions to solve data science challenges.
Data Exploration
Data Exploration refers to exploring the large data to uncover the patterns or trends needed for the AI
project.
It is considered to be the first step in data analysis where unstructured data is explored, researched,
filtered and visualised to decide the strategy for the type of model used in the later stage.
For example, if you have to buy a laptop, you need to explore your requirements of the configuration
that you want, of the RAM, hard disk, processor, operating system, graphic card, touch screen or not, etc.
But from this unstructured data you have to choose the one that suits the best to your needs. Similarly,
after the data is acquired, it needs to be explored to suit the needs of the AI Project.
Visualisation of the data plays a very important role in data analysis. This visualisation process has to be
carried in some user-friendly format so that you can:
• Quickly get a sense of the trends, relationships and patterns contained within the data.
• Define strategy for which model to use at a later stage.
• Communicate the same to others effectively.
Components of AI Project Framework 167

