**Best techniques to handle missing data**

- Use deletion methods to eliminate missing data. The deletion methods only work for certain datasets where participants have missing fields.
- Use regression analysis to systematically eliminate data.
- Data scientists can use data imputation techniques.

## How do you deal with missing data in Analytics?

Techniques for Handling the Missing Data

- Listwise or case deletion.
- Pairwise deletion.
- Mean substitution.
- Regression imputation.
- Last observation carried forward.
- Maximum likelihood.
- Expectation-Maximization.
- Multiple imputation.

## How can missing data be resolved?

Listwise Deletion: Delete all data from any participant with missing values. If your sample is large enough, then you likely can drop data without substantial loss of statistical power. Be sure that the values are missing at random and that you are not inadvertently removing a class of participants.

## What is a useful strategy to use when you are missing data?

Answer: Multiple imputation is another useful strategy for handling the missing data. In a multiple imputation, instead of substituting a single value for each missing data, the missing values are replaced with a set of plausible values which contain the natural variability and uncertainty of the right values.

## How does Business Intelligence deal with missing data?

The first approach is to replace the missing value with one of the following strategies:

- Replace it with a constant value.
- Replace it with the mean or median.
- Replace it with values by using information from other columns.

## What happens when dataset includes missing data?

Explanation: However, if the dataset is relatively small, every data point counts. In these situations, a missing data point means loss of valuable information. In any case, generally missing data creates imbalanced observations, cause biased estimates, and in extreme cases, can even lead to invalid conclusions.

## How do you get rid of missing values?

Removing Data. When dealing with missing data, data scientists can use two primary methods to solve the error: imputation or the removal of data. The imputation method develops reasonable guesses for missing data. It’s most useful when the percentage of missing data is low.

## How do you find the missing value of a data set?

Conclusion

- Use isnull() function to identify the missing values in the data frame.
- Use sum() functions to get sum of all missing values per column.
- use sort_values(ascending=False) function to get columns with the missing values in descending order.
- Divide by len(df) to get % of missing values in each column.

## What should a data analyst do with missing or suspected data?

What should a data analyst do with missing or suspected data? In such a case, a data analyst needs to: Use data analysis strategies like deletion method, single imputation methods, and model-based methods to detect missing data. Replace all the invalid data (if any) with a proper validation code.

## How do you handle missing or 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.

## How do you fill missing categorical data?

There is various ways to handle missing values of categorical ways.

- Ignore observations of missing values if we are dealing with large data sets and less number of records has missing values.
- Ignore variable, if it is not significant.
- Develop model to predict missing values.
- Treat missing data as just another category.