Clean up the m_ssing data

"Garbage in, garbage out". Do not produce bad report because of bad data. Welcome to new package, CleanData, solution for solving the missing data in your report. The package is free to download.

Problem

Missing data is always a problem in data analysis in general and report making in particular. It causes:

1. Errors in your report. Missing data comes with NaN or null value which cause trouble for calculation.
2. Wrong or bias result. For example,null value will drive the average down.

Solution

The missing data can be solved generally in two ways. You can cancel the rows with missing data or fill the missing data with reasonable value. The CleanData package provide all two solutions though two classes DropNull and FillNull.

The DropNull has capability of detecting null value in your data table. We can detect null in all columns or just some of specific columns either by column names, column types (number,string, datetime). The DropNull also take into consideration of how serious the missing data is. We can set the threshold which is the minimum number of missing values in a row to be consider being dropped.

The FillNull also detects the null value in all columns or in particular column names or types. It will try to replace the null value with a new value. That's value can be set by us for example we want to replace all null value with 0. Or we can replace missing value with smarter number such as MEAN or MEDIAN. FillNull can helps you to do that.

->pipe(new FillNull(array(
    "newValue"=>FillNull::MEAN,
    "targetColumnType"=>"number",
)))

Summary

The CleanData package is a good solution for dataset with missing data. In the future, we would like to apply some AI technique to better fill data to the missing one. The AI could suggest a reasonable value based on its observation of relation among surrounding data. KoolReport will be best report tool for php.

<3 koolreport team