Missing Data (DP_2)

This functionality handles missing data using various techniques. e.g mean, mode and more.

Missing data can pose significant challenges in machine learning because many algorithms cannot handle missing values. Therefore, before applying a machine learning algorithm to a dataset with missing values, the missing data must be addressed through some form of data imputation, which involves estimating the missing values from the available data.

There are various techniques for imputing missing data, such as mean imputation, mode imputation, regression imputation, and more advanced methods. The choice of imputation technique depends on the nature of the missing data and the goals of the analysis. However, it is essential to handle missing data appropriately to prevent biases and errors in the machine learning model.

Sample Request

This request uses the mean strategy to fill in missing values in the second column to the end with the mean values of the X variable.

{
    "project_id": 1,
    "parent_id": 1,
    "block_id": 2,
    "function_code": "DP_2",
    "args": {
        "strategy_value": "mean",
        "boundaries": ":, 2:"
    }
}

Missing Data

Handle missing data

POST https://autogon.ai/api/v1/engine/start

Handles missing data rows in the dataset either by deletion of such rows or filling in with a specified method.

Request Body

NameTypeDescription

project_id*

int

current project ID

parent_id*

int

parent block ID

block_id*

int

current block ID

function_code*

String

block's function code

strategy_value*

String

strategy for handling missing data

boundaries

String

slicing boundaries for x features

args

object

block arguments

{
    "status": "true",
    "message": {
        "id": 2,
        "project": 1,
        "block_id": 2,
        "parent_id": 1,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": ""
    }
}
// Some code

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