Predict housing prices with a simple neural network
# Predict Housing Prices with a Neural Network
This n8n template demonstrates how a simple Multi-Layer Perceptron (MLP) neural network can predict housing prices. The prediction is based on four key features, processed through a three-layer model.
### Input Layer
Receives the initial data via a webhook that accepts four query parameters.
### Hidden Layer
Composed of two neurons. Each neuron calculates a weighted sum of the inputs, adds a bias, and applies the ReLU activation function.
### Output Layer
Contains one neuron that calculates the weighted sum of the hidden layer's outputs, adds its bias, and returns the final price prediction.
## Setup
This template works out-of-the-box and requires no special configuration or prerequisites. Just import the workflow to get started.
## How to Use
Trigger this workflow by sending a GET request to the webhook endpoint. Include the house features as query parameters in the URL.
Endpoint: `/webhook/regression/house/price`
### Query Parameters
- `square_feet`: The total square footage of the house.
- `number_rooms`: The total number of rooms.
- `age_in_years`: The age of the house in years.
- `distance_to_city_in_km`: The distance to the nearest city center in kilometers.
### Example
Here’s an example curl request for a 1,500 sq ft, 3-room house that is 10 years old and 5 km from the city.
#### Request
```
curl "https://your-n8n-instance.com/webhook/regression/house/price?square_feet=1500&number_rooms=3&age_in_years=10&distance_to_city_in_km=5"
```
### Response
```
JSON
{
"price": 53095.832123960805
}
```
