How to Impute Missing Value in Machine Learning

Data

Whenever we work on any project or problem, data is the main object, without it we cannot make a solution or prediction or data has a main role in data science. The more data there is, the better the model will be.

Imputation

Imputation is a technique in which we fill missing data in different ways.

Note. In machine learning, data is either collected manually or we have to buy it from the market. Now in our data, there are many data points which are missing or wrongly filled. To ensure the purity of the data or to make the model, we apply imputation technique data in machine learning.

There are many effective methods of machine learning imputation: Hare are some of the most effective techniques:

1. Mean, Median and Mode Imputation

Mean Imputation: In order to replace the missing value, mean imputation techniques are applied which are available from all the data and it can distort the data distribution, especially with a high percentage of missing value.

Median Imputation: The values ​​were the median value, which is more robust if the outliers were covered. This method is particularly useful when the data contains extreme values.

Mode Imputation: For categorical data, replace missing values ​​with the most frequent value mode can be effective.

2. K Nearest Neighbors (KNN) Imputation

By taking advantage of the k nearest neighbors per data point constant or non-missing value, we take a mode fills the points that are nearby. This method can capture the complex relationship between data and data points.

Regression Imputation: This method involves predicting missing values ​​using regression models based on other features in the dataset. It provides jida accurate imputations only a little tweaking has to be done in model selection.

Multiple Imputation: Multiple imputation creates Many different imputed datasets by filling in missing values ​​multiple times based on statistical models. Each dataset is analyzed separately.

This approach is particularly effective for datasets with a substantial amount of missing information.

Interpolation Techniques: For time-series data, methods like linear interpolation estimate missing values ​​by averaging adjacent known points. for the missing values ​​the imputation can be done by averaging the adjacent values.

Forward Fill and Backward Fill: In this technique, the next point of the data or the previous point is marked or filled, making them suitable for ordered datasets such as time series. Forward fill replaces a missing value with the last observed value, while backward fill uses the next available value.

Conclusion

The imputation method depends on various factors like what is the data, what is its type or how many missing values ​​are there. We apply imputation method on the type of data, if some points which are outliers are also removed.