# Simple Linear Regression (ML\_R\_1)

It uses a linear technique to predict the value of the dependent variable based on the value of the independent variable. 

The values of the coefficients of the equation are found by minimizing the difference between the observed values of the dependent variable and the predicted values by the equation. 

It can help understand the relationship between two variables and make 

## Sample Request

Build a simple linear regression model named, *"SimpleModel"*

```javascript
{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_R_1",
    "args": {
        "model_name": "SimpleModel"
    }
}
```

## Building a Simple Linear Regression model

## Simple Linear Regression

<mark style="color:green;">`POST`</mark> `https://autogon.ai/api/v1/engine/start`

#### Request Body

| Name                                             | Type   | Description                                  |
| ------------------------------------------------ | ------ | -------------------------------------------- |
| project\_id<mark style="color:red;">\*</mark>    | int    | The `id` of the current project              |
| block\_id<mark style="color:red;">\*</mark>      | int    | The `id` of the current block                |
| function\_code<mark style="color:red;">\*</mark> | string | The function code for current block          |
| parent\_id<mark style="color:red;">\*</mark>     | int    | The `id` of the previous block               |
| args                                             | object | Block arguments                              |
| model\_name                                      | String | Name of the model to be used for prediction. |

{% tabs %}
{% tab title="200 Statemanagement object" %}

```javascript
{
    "status": "true",
    "message": {
        "id": 8,
        "project": 1,
        "block_id": 8,
        "parent_id": 7,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": "{\"SimpleModel\": {\"function_code\": \"ML_R_1\", \"model_url\": ""}}"
    }
}
```

{% endtab %}
{% endtabs %}

{% tabs %}
{% tab title="Python" %}

```python
```

{% endtab %}

{% tab title="Node" %}

```javascript
const project_id = 1
const parent_id = 7
const block_id = 8

simpleLinearRegression = await client.simple_linear_regression(project_id, parent_id, block_id, {
    model_name: "SimpleModel"
});
```

{% endtab %}
{% endtabs %}

## Sample Request

Make predictions with the pre-built model passing an optional test data.

```javascript
{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "ML_R_1_P",
    "args": {
        "model_name": "SimpleModel",
        "test_data": ""
    }
}
```

## Predicting with Simple Linear Regression

## Simple Linear Regression Predict

<mark style="color:green;">`POST`</mark> `https://autogon.ai/api/v1/engine/start`

#### Request Body

| Name                                          | Type   | Description                                                            |
| --------------------------------------------- | ------ | ---------------------------------------------------------------------- |
| model\_name<mark style="color:red;">\*</mark> | String | Name of previously trained model to be used for prediction             |
| test\_data                                    | String | Input data for prediction. Defaults to `x_train_url` in StateManagment |
| project\_id                                   | int    | ID of the current project                                              |
| block\_id                                     | int    | ID of the current block                                                |
| parent\_id                                    | int    | ID of the previous block                                               |
| function\_code                                | String | Function code for the current block                                    |

{% tabs %}
{% tab title="200: OK Statemanagement object" %}

```javascript
{
    "status": "true",
    "message": {
        "id": 9,
        "project": 1,
        "block_id": 9,
        "parent_id": 8,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": "{\"y_pred_url\": ""}"
    }
}
```

{% endtab %}
{% endtabs %}

{% tabs %}
{% tab title="Python" %}

```
// Some code
```

{% endtab %}

{% tab title="Node" %}

```javascript
const project_id = 1
const parent_id = 7
const block_id = 8

simpleLinearRegressionPredict = await client.simple_linear_regression_predict(project_id, parent_id, block_id, {
     model_name: "SimpleModel"
     test_data: null
});
```

{% endtab %}
{% endtabs %}


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