Regression

It may be defined as the parametric technique that allows us to make decisions based upon data or in other words allows us to make predictions based upon data by learning the relationship between input and output variables. Here, the output variables dependent on the input variables, are continuous-valued real numbers. In regression, the relationship between input and output variables matters and it helps us in understanding how the value of the output variable changes with the change of input variable. Regression is frequently used for prediction of prices, economics, variations etc.

Linear Regressor/Single Variable Regressor

Let's see how to build single variable regressor.  First we import a few required packages:

import numpy as np
from sklearn import linear_model
import sklearn.metrics as sm
import matplotlib.pyplot as plt

Now, we need to provide the input data and we have saved our data in the file named linear.txt.

input = 'D:/ProgramData/linear.txt'

We need to load this data by using the np.loadtxt function.

input_data = np.loadtxt(input, delimiter=',')
X, y = input_data[:, :-1], input_data[:, -1]

The next step would be to train the model. Let us give training and testing samples.

training_samples = int(0.6 * len(X))
testing_samples = len(X) - num_training
X_train, y_train = X[:training_samples], y[:training_samples]
X_test, y_test = X[training_samples:], y[training_samples:]

Now, we need to create a linear regressor object.

reg_linear= linear_model.LinearRegression()
Train the object with the training samples.
reg_linear.fit(X_train, y_train)

We need to do the prediction with the testing data.

y_test_pred = reg_linear.predict(X_test)

Now plot and visualize the data.

plt.scatter(X_test, y_test, color='red')
plt.plot(X_test, y_test_pred, color='black', linewidth=2)
plt.xticks(())
plt.yticks(())
plt.show()

The output will be as shown below:

The performance of our linear regression can be computed as follows:

print("Performance of Linear regressor:")
print("Mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred), 2))
print("Mean squared error =", round(sm.mean_squared_error(y_test, y_test_pred), 2))
print("Median absolute error =", round(sm.median_absolute_error(y_test, y_test_pred), 2))

print("Explain variance score =", round(sm.explained_variance_score(y_test, y_test_pred), 2))
print("R2 score =", round(sm.r2_score(y_test, y_test_pred), 2))

Output

Performance of Linear Regressor:
Mean absolute error = 1.78
Mean squared error = 3.89
Median absolute error = 2.01
Explain variance score = -0.09
R2 score = -0.09

In the above code, we have used this small data. If you want some big dataset then you can use sklearn.dataset to import bigger dataset.

2,4.82.9,4.72.5,53.2,5.56,57.6,43.2,0.92.9,1.92.4,3.50.5,3.41,40.9,5.91.2,2.583.2,5.65.1,1.54.5,1.22.3,6.32.1,2.8

So in this way we can build a single variable regressor. In the next post we'll see how to build multi-  variable regressor.