Handling missing values and outlier treatment is vey crucial for any data science project during the data preprocessing phase. It directly impact the quality of data and the outcome of trained model on this data.
During Data Preprocessing phase there is always confusion about whether we should process the missing data imputation or outliers? In this article, we will try to putlight on this to avoid the confusion during your data preprocessing job.
In most cases,
It is recommended to perform missing values handling before outlier handling.
Here’s why:
- Data Integrity: Missing values can affect the integrity of your dataset and subsequent analysis. By addressing missing values first, you ensure that the remaining data is more complete and representative of the original dataset.
- Outlier Impact: Outliers are extreme values that can significantly affect statistical measures and analysis results. When dealing with missing values, it’s important to consider how outliers might affect imputation techniques or analysis of the missingness pattern. By removing or imputing missing values first, you can better assess the impact of outliers on subsequent steps.
- Outlier Detection Methods: Outlier detection methods often rely on complete data to accurately identify outliers. If missing values are present, it can affect the performance and accuracy of outlier detection techniques. Therefore, addressing missing values before outlier detection allows for a more reliable analysis of outliers.
- Data Imputation: If you choose to impute missing values, the method you use can be influenced by the presence of outliers. Outliers might skew the imputation process, leading to biased imputed values. By handling missing values first, you can ensure that the imputation process is performed on a more representative dataset.
That being said, the order of these steps may vary depending on the specific characteristics of your dataset and the requirements of your analysis. It’s important to consider the nature of missing values and outliers in your data and make an informed decision based on your specific project goals.
