Predict Prices By Using Linear Regression Algorithm In Machine Learning

Simple linear regression is a statistical method that allows us to summarize and study relationships between two continuous variables where the output of variable is directly proportional to input. The variable which we will be predicting is called criterion variable which is along the Y axis and the variable which we are basing our predictions is known as predictor which is along the X axis. When there is single predictor variable that method is called simple linear regression method and when we have more than 1 predictor that model is called multiple linear regression method.

Statistical relation has lot of examples like height and weight, speed and petrol consumption, router bandwidth and router cpu relation, pizza size and price relation etc.

In the previous post, we have used the KNN neighbor algorithm to predict the values. In this post, we will be using the simple linear regression method to predict the pizza prices.

Before moving to the machine learning code, we need to first understand what equation of line is. The equation of line represents the relationship between the X and Y axis. Formula of finding equation of line is y = mx + c

C is Y intercept where the line meets the Y axis.

Slope of the line is nothing but the difference between the (Y2 – Y1)/(X2-X1)

With the above equation, if we get the values of m and c which remains constant and for every value of x we can get the value of y. Now let’s take example of pizza size and its price. First plot the variables with the existing dataset and after that we will use the simple linear regression to predict the pizza prices by giving pizza size.

Below is the graph of pizza size vs pizza price. This example is the right fit for simple linear regression method because as the size increase the price is also increasing. So we can say that there is direct relationship between pizza size and price.

Now we have to find the values of m and c, so that we can find predict any new price of any size of pizza. To get this done, we will be using the Scikit Learn library and import the linear regression model and find the slope and y intercept value.

Use the below python code in Jupyter Notebook and predict the price value of pizza sizes from 100 to 110.

import matplotlib.pyplot as plt
from sklearn import linear_model
pizza_size = [[4.0],[4.5],[5.0],[5.6],[5.8],[6.0],[7.2],[7.4],[7.8],[9]]
pizza_price = [42,45,50,55,58,62,65,70,72,75]
print("Pizza Size and Pizza Price")
for row in zip(pizza_size,pizza_price):
print(row[0],'->',row[1])

#Instantiating Linear Regression Model
reg = linear_model.LinearRegression()
reg.fit(pizza_size,pizza_price)

#storing the slope
m = reg.coef_
#storing y intercept
b = reg.intercept_

print("Slope Of the Line Is:", m)
print("Y intercept is:", b)

# This is used to plot the existing relationship on graph
plt.scatter(pizza_size,pizza_price,color='red')
#Equation of straight Line is y = m*x + b
#Now we know the m and b values, so we can predict the straight line points
predicted_values = [m * x + b for x in pizza_size]

#Plot the straight line
plt.plot(pizza_size,predicted_values,'r--')
plt.xlabel("pizza_size")
plt.ylabel("pizza_price")
plt.show()

#Predict the Pizza Size Prices from 100 to 110
for i in range(100,110):
print("The price of pizza will be:",reg.predict(i))

People who read this post also read :