datasets are used from http://college.cengage.com/mathematics/brase/understandable_statistics/7e/students/datasets/mlr/frames/frame.html

run any python3 health.py , bicarbonate.py or hollywood.py for the already implemented datasets

In health.py dataset contents are X1 = death rate per 1000 residents X2 = doctor availability per 100,000 residents X3 = hospital availability per 100,000 residents X4 = annual per capita income in thousands of dollars X5 = population density people per square mile

In bicarbonate.py dataset X = pH of well water Y = Bicarbonate (parts per million) of well water

In hollywood.py dataset X1 = first year box office receipts/millions X2 = total production costs/millions X3 = total promotional costs/millions

The regression.py file implements the Regression class where it can calculate a problem. The data is entered in the class initialization model = Regression (y, x)

To enter the data y must be an NumPy array of n lines and 1 column and this will be the target of our prediction.

x should be a table of NumPy n lines equal to y and m columns where the different attributes will be for a Multiple Variable Regression

After the data entry, the model needs training For training we use the train (a, epochs, print_loss) function where a is the learning rate epochs are the iterations of the learning process print_loss is a True or False value if we want to print it in real time the loss of the regression

And finaly to predict the results we use the predict function predict(x1, x2 ...) which input are the X different attributes from which we want to arrive at a prediction Y

Hollywood movies revenue prediction based on production and promotion costs

and loss in epochs

Prediction in ppm of bicarbonates based on ph of water