Create a Linear Regression Model in Python using a randomly created data set.
Linear Regression Model
Linear regression Lazyroar for Lazyroar
Generating the Training Set
# python library to generate random numbers from random import randint # the limit within which random numbers are generated TRAIN_SET_LIMIT = 1000 # to create exactly 100 data items TRAIN_SET_COUNT = 100 # list that contains input and corresponding output TRAIN_INPUT = list() TRAIN_OUTPUT = list() # loop to create 100 data items with three columns each for i in range(TRAIN_SET_COUNT): a = randint(0, TRAIN_SET_LIMIT) b = randint(0, TRAIN_SET_LIMIT) c = randint(0, TRAIN_SET_LIMIT) # creating the output for each data item op = a + (2 * b) + (3 * c) TRAIN_INPUT.append([a, b, c]) # adding each output to output list TRAIN_OUTPUT.append(op) |
Machine Learning Model – Linear Regression
The Model can be created in two steps:-
1. Training the model with Training Data
2. Testing the model with Test Data
Training the Model
The data that was created using the above code is used to train the model
# Sk-Learn contains the linear regression model from sklearn.linear_model import LinearRegression # Initialize the linear regression model predictor = LinearRegression(n_jobs =-1) # Fill the Model with the Data predictor.fit(X = TRAIN_INPUT, y = TRAIN_OUTPUT) |
Testing the Data
The testing is done Manually. Testing can be done using some random data and testing if the model gives the correct result for the input data.
# Random Test data X_TEST = [[ 10, 20, 30 ]] # Predict the result of X_TEST which holds testing data outcome = predictor.predict(X = X_TEST) # Predict the coefficients coefficients = predictor.coef_ # Print the result obtained for the test data print('Outcome : {}\nCoefficients : {}'.format(outcome, coefficients)) |
The Outcome of the above provided test-data should be, 10 + 20*2 + 30*3 = 140.
Output
Outcome : [ 140.] Coefficients : [ 1. 2. 3.]
