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In this chapter, we will focus on the basic example of linear regression implementation using TensorFlow. Logistic regression or linear regression is a supervised machine learning approach for the classification of order discrete categories. Our goal in this chapter is to build a model by which a user can predict the relationship between predictor variables and one or more independent variables.

The relationship between these two variables is cons −idered linear. If y is the dependent variable and x is considered as the independent variable, then the linear regression relationship of two variables will look like the following equation −

Y = Ax+b

We will design an algorithm for linear regression. This will allow us to understand the following two important concepts −

- Cost Function
- Gradient descent algorithms

The schematic representation of linear regression is mentioned below −

The graphical view of the equation of linear regression is mentioned below −

We will now learn about the steps that help in designing an algorithm for linear regression.

It is important to import the necessary modules for plotting the linear regression module. We start importing the Python library NumPy and Matplotlib.

import numpy as np import matplotlib.pyplot as plt

Define the number of coefficients necessary for logistic regression.

number_of_points = 500 x_point = [] y_point = [] a = 0.22 b = 0.78

Iterate the variables for generating 300 random points around the regression equation −

Y = 0.22x+0.78

for i in range(number_of_points): x = np.random.normal(0.0,0.5) y = a*x + b +np.random.normal(0.0,0.1) x_point.append([x]) y_point.append([y])

View the generated points using Matplotlib.

fplt.plot(x_point,y_point, 'o', label = 'Input Data') plt.legend() plt.show()

The complete code for logistic regression is as follows −

import numpy as np import matplotlib.pyplot as plt number_of_points = 500 x_point = [] y_point = [] a = 0.22 b = 0.78 for i in range(number_of_points): x = np.random.normal(0.0,0.5) y = a*x + b +np.random.normal(0.0,0.1) x_point.append([x]) y_point.append([y]) plt.plot(x_point,y_point, 'o', label = 'Input Data') plt.legend() plt.show()

The number of points which is taken as input is considered as input data.

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