In the previous blob, I used scikit-learn linear regression for one feature. This time, I tried multi-linear regression, meaning the dataset has multiple variables.
<pre><code class="lang-python">import numpy as np
from sklearn.linear_model import SGDRegressor
x_train = np.array([[1.0, 2.0, 3.0],
 [4.0, 2.0, 2.0],
 [5.0, 1.0, 4.0]]) #features
y_train = np.array([300.0, 500.0, 700.0]) 

sgdr.fit(x_train, y_train)
print(sgdr)
b_norm = sgdr.intercept_
w_norm = sgdr.coef_
print(f"model parameters: w: {w_norm}, b:{b_norm}")
</code></pre>
Here is the printed value
<pre><code class="lang-python">model parameters: 
w: [87.03533013 9.0616632 60.34296761], b:[13.81946812]
</code></pre>
If you look at the dataset, the first feature has the strongest correlation, and the third one has the second largest correlation. Therefore, w1 is the largest value, and w3 is the second largest.

In the previous blob, I used scikit-learn linear regression for one feature. This time, I tried multi-linear regression, meaning the dataset has multiple variables.

```python
import numpy as np
from sklearn.linear_model import SGDRegressor
x_train = np.array([[1.0, 2.0, 3.0],
                   [4.0, 2.0, 2.0],
                   [5.0, 1.0, 4.0]])   #features
y_train = np.array([300.0, 500.0, 700.0]) 

sgdr.fit(x_train, y_train)
print(sgdr)
b_norm = sgdr.intercept_
w_norm = sgdr.coef_
print(f"model parameters:   w: {w_norm}, b:{b_norm}")
```

Here is the printed value

```python
model parameters: 
w: [87.03533013  9.0616632  60.34296761], b:[13.81946812]
```

If you look at the dataset, the first feature has the strongest correlation, and the third one has the second largest correlation. Therefore, w1 is the largest value, and w3 is the second largest.

Linear regression with multi-features