Start learning 50% faster. Sign in now
Data cleaning focuses on resolving inconsistencies, filling missing values, removing duplicates, and handling outliers to ensure the dataset is accurate, complete, and reliable. High-quality data is critical for generating meaningful insights and avoiding analytical errors. For example, incorrect or incomplete customer information in a sales dataset could lead to flawed marketing strategies. Techniques such as imputation, deduplication, and outlier treatment ensure the dataset is ready for analysis. Clean data enables better decision-making and enhances the credibility of data-driven insights. Why Other Options Are Incorrect: • A: Data transformation involves reformatting or scaling data, not cleaning it. • C: Cleaning prepares data for visualization but is not specifically aimed at visualization. • D: Standardization may occur during cleaning but is not its sole purpose. • E: While validation is related to accuracy, cleaning focuses more broadly on quality improvement.
Kindly Study the following questions carefully and choose the right answer.
In plants, the carbohydrates which are not used immediately are sto...
When carbon dioxide gas is passed through lime water, a white precipitate is formed, which dissolves on
passing excess of carbon dioxide. The...
Two waves, each of amplitude 1.5 mm and frequency 10 Hz, are travelling in opposite direction with a speed of 20 mm/s. The distance in mm between adjace...
What is the common name of calcium hydroxide?
Which of the following is a primary pollutant?
Which of the following gas usually causes explosions in coal-mines?
Which of the following is NOT a method to improve crop yields in India?
Which of the following compounds form nitrites with nitrous acid?
An object is placed on the axis of a concave mirror at its focus (F). The image formed is: