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What is Data cleansing?

Data cleansing, also known as data scrubbing, is the process of identifying and correcting errors, inconsistencies, and duplicate records within a dataset. It enhances data quality, improves accuracy in analytics, and ensures reliable outcomes in AI models and business intelligence systems.

Raw data
Raw data refers to unprocessed information collected from various sources, such as sensors, databases, or surveys. It lacks structure and requires cleaning, transformation, and analysis before being used in decision-making, machine learning, or statistical modeling.
AI Agents
AI agents are autonomous systems that perceive their environment, make decisions, and take actions to achieve specific goals. They use artificial intelligence (AI) techniques such as machine learning, natural language processing (NLP), and reinforcement learning to adapt and improve their performance over time. AI agents are widely used in various applications, including chatbots, recommendation systems, robotic automation, and autonomous vehicles.
Relational Database
Relational database refers to a structured data storage system that organizes data into tables with predefined relationships. It uses SQL for querying and is widely used in enterprise applications, transaction processing, and analytics.

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