Data cleaning and preparation are crucial steps in the data analysis process, aimed at improving data quality and ensuring that the data is suitable for accurate and reliable analysis. Several techniques are used during data cleaning and preparation to enhance data quality. Let's explore some of these techniques:
1. Handling Missing Data:
One common issue in datasets is missing data, which can occur due to various reasons such as data entry errors or incomplete responses. Techniques to handle missing data include imputation methods like mean, median, mode, or regression imputation. Another approach is to remove records with missing values, but this should be done with caution to avoid bias.
2. Removing Duplicates:
Duplicate records can distort the analysis results and mislead the interpretation. Removing duplicates ensures that each data point is unique and avoids duplicative influence on the analysis.
3. Outlier Detection and Treatment:
Outliers are data points that significantly deviate from the majority of the data. Outliers can arise due to errors or represent exceptional cases. Detecting and handling outliers helps prevent their undue influence on statistical analysis and modeling.
4. Standardization and Normalization:
Standardizing and nor....
Log in to view the answer
