In the previous topic, we dealt with linear regression with one variable. Now we will learn an extended version of linear regression, where we will use multiple input variables to predict the output.
We will rely on examples where we will load and predict stock prices. Therefore, we will experiment with the main libraries used for loading stock prices.
If you recall the formula for the line of best fit in linear regression, it was defined as y = a*x + b, where a is the slope of the line, b is the y-intercept of the line, x is the feature value, and y is the calculated label value.
In multiple regression, we have multiple features and one label. Assuming that we have three features, x1, x2, and x3, our model changes as follows:
y = a1 * x1 + a2 * x2 + a3 * x3 + b
In NumPy array format, we can write this equation as follows:
y = np.dot(np.array([a1, a2, a3]), np.array([x1, x2, x3])) + b
For convenience, it makes sense to define the...