How to fill null values in a dataset

Handling missing or null values in a dataset is a common challenge in data analysis. Incomplete or missing data can impact the accuracy of analysis and modeling results. Here are some common methods for filling null values in a dataset:

1. Mean/Median/Mode Imputation: In this method, the missing values are replaced by the mean, median, or mode of the non-null values in the same column. This is a simple method and works well when the data is normally distributed.
2. KNN Imputation: This method uses k-nearest neighbors to fill in missing values. The idea is to find the k nearest neighbors of each missing value and fill the missing value with the mean of the values from these k neighbors.
3. Linear Interpolation: In this method, the missing values are filled by linear interpolation between the values of the nearest neighbors. This method is best suited for time-series data.
4. Multiple Imputation: In this method, multiply imputed datasets are created and analyzed to obtain more accurate results. The missing values are randomly filled multiple times and averaged to obtain the final results.
5. Predictive Modeling: In this method, a predictive model is trained on the available data to predict the missing values. The model can be linear regression, decision trees, or any other machine learning algorithm.

In conclusion, filling in missing values in a dataset is a crucial step in data analysis. The method used for filling in missing values can impact the accuracy of the results, so it is important to choose the right method and use it appropriately.