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Data cleaning is an essential first step after collecting raw data, ensuring the dataset is accurate, consistent, and usable. Cleaning involves handling missing values, removing duplicates, correcting inaccuracies, and standardizing formats. For example, in a customer churn analysis, incomplete demographic information, inconsistent subscription statuses, or duplicate entries could skew results. By addressing these issues upfront, the data analyst lays a solid foundation for reliable analysis, avoiding errors in downstream processes such as EDA, modeling, or visualization. Cleaning ensures data integrity, which is critical for building models or interpreting trends accurately. Why Other Options Are Incorrect: • A: Building predictive models without clean data can lead to flawed or unreliable predictions. • B: EDA should follow data cleaning to ensure the trends and patterns observed are valid. • C: Visualization comes after data analysis and modeling, not before. • D: KPIs should be defined during the planning phase, before collecting and cleaning data.
Which nutrient is responsible for the pollen germination in wheat crop?
What is etiolation?
Which one of the following is function of calcium?
a. Involved in the structure of chlorophyll
b. Maintains the shape of cell membrane
...Which parasite attaches mainly to the gills of sea bass and causes hyperplasia and necrosis?
India is known as the land of
Which one of the following is the data distribution when mean and median values are same?
Which among the following statement is not true?
The following are the steps of chemiosmotic ATP synthesis in the light reaction. Arrange them in correct order of their occurence-
(A) H diffuse ...
The oil content in Groundnut is:
…………….. refers to removal of field heat (quick cooling) after harvest; if not, its deterioration is faster at higher temperature of 1 hour at ...