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

Name	Type	Description
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

200: OK Missing Data Successful

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

Python

// Some code

Node