Predict

Predict the pricing of a product based on specified attributes.

/price/predict

Example

curl --request POST \
  --url https://api.bencha.io/v1/price/predict \
  --header 'Authorization: Bearer {api_key}' \
  --header 'content-type: application/json' \
  --data '{
    "query": {
      "brand": "newbie",
      "category": "tops",
      "target": "mixed"
    }
  }'

Body

Query attributes

Brand and category

If an identifier is provided for the brand or category, it will be used directly in the prediction. If not, the API will perform a search based on the provided brand or category name through the /search/brands or /search/categories endpoints.

Material

Defines the composition or fabric type of the item. Each material affects the product’s perceived quality, durability, and price estimation.

Condition

Defines the state or quality of the product being priced. You can select from the following options (best to worse):

Demography

Indicates the target demographic for the product. Choose from:

Target

Specifies the pricing segment you’re aiming for in your predictions. Select from the following:

For more information on how these targets relate to data handling, refer to the fill_mode section.

Method

Describes the sales method used for the product:

Status

Represents the listing status of the product:

Reference

An optional identifier used to reference a related item or entity from either an internal or external system. This field may be used to assist in matching, linking, or influencing price prediction. It can be any recognizable string such as an ID, code, or label.

Additional Parameters

Currency

Specifies the currency in which the predicted price will be returned. The value should be a valid ISO 4217 currency code, e.g. "SEK" for Swedish Krona.

Model

The model parameter specifies the model to be used for generating price predictions. Select from one of our various pre-trained models tailored for different scenarios or datasets.

Selectable Models:

Formatting

Formatting controls how numeric values are presented in responses, ensuring consistency and precision. It allows precise control over the number of fraction digits displayed and the rounding behavior applied. By configuring min_fraction_digits and max_fraction_digits, you can enforce consistent precision, while rounding_increment and rounding_mode determine how values are rounded to generate specific price points.

Fraction digits

Specifies the minimum and maximum number of fraction digits in the response. By default, it uses the selected currency’s minor unit digits.

Rounding

Specifies how numeric values should be rounded. rounding_increment controls the rounding step size, rounding_targets optionally specifies target minor-unit values to round to, and rounding_mode determines the rounding method. When rounding_targets is provided, numbers are rounded to the nearest target value instead of the standard increment.

{
  "rounding_increment": 50,
  "rounding_mode": "ceil"
}

{
  "rounding_targets": [49, 99]
}

{
  "rounding_targets": [4500, 9500]
}

Fine Tuning

Fine-tuning allows customization to the behavior of the model, ensuring that it aligns more closely with specific use cases or market conditions. By adjusting certain parameters, you can enhance the model’s performance, leading to more accurate and reliable predictions.

Fill Mode

The fill mode parameter determines how the model handles missing or undesirable values during the prediction process. The default value is mixed and the available options are:

Tolerance

The tolerance parameter controls the level of adaptability in the model’s suggestions. It determines how much the model can adjust to the input data, offering flexibility for different use cases.

Biases

The biases parameters allows for adjustment to the model’s predictions, helping to mitigate the impact of possible outliers or skewed data distributions.

Response