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.
Four fold food production occurred in
Which of the following statement is incorrect?
The amount of basalin 45 EC for a plot of 50 m x 40 m at recommended rate of fluchloralin at 0.75 kg/ha is
Bacterial leaf blight of rice is caused by
In general, the problems ascribed to be created by the use of chemical fertilizers include(s):
How many days are taken by a fry to grown into a fingerling?
Which of the following is appropriate for a clay soil?
Herbicide A and herbicide B was applied on plot I and plot II respectively. The weed index of plot I is 70 % and Plot II is 73 %. What are the inference...
The term Retting is associated with which crop?
Trichoderma viride, a bioagent to control some diseases in crop plants, acts as…………………….
