How do you handle missing values or outliers in dataset?
There are basically three methods for treating outliers in a data set. One method is to remove outliers as a means of trimming the data set. Another method involves replacing the values of outliers or reducing the influence of outliers through outlier weight adjustments.
How do you handle missing and corrupted data in a dataset?
how do you handle missing or corrupted data in a dataset?
- Method 1 is deleting rows or columns. We usually use this method when it comes to empty cells.
- Method 2 is replacing the missing data with aggregated values.
- Method 3 is creating an unknown category.
- Method 4 is predicting missing values.
What are the possible reasons for missing values in the dataset?
The real-world dataset often has a lot of missing values. The cause of the presence of missing values in the dataset can be loss of information, disagreement in uploading the data, and many more. Missing values need to be imputed to proceed to the next step of the model development pipeline.
Why are there missing values in my dataset?
This could be due to many reasons such as data entry errors or data collection problems. Irrespective of the reasons, it is important to handle missing data because any statistical results based on a dataset with non-random missing values could be biased. Also, many ML algorithms do not support data with missing values.
How can we add variance to a data set?
This strategy can be applied on a feature which has numeric data like the age of a person or the ticket fare. We can calculate the mean, median or mode of the feature and replace it with the missing values. This is an approximation which can add variance to the data set.
What are the different types of missing data?
There are three main types of missing data: 1 Missing completely at random (MCAR) 2 Missing at random (MAR) 3 Not missing at random (NMAR)
How to treat missing values in your data-data science?
Imputation of missing values is a tricky subject and unless the missing data is not observed completely at random, imputing such missing values by a Predictive Model is highly desirable since it can lead to better insights and overall increase in performance of your predictive models.