Created
July 25, 2022 05:09
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Basic ML Understanding Code
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| from sklearn.linear_model import LinearRegression | |
| import random | |
| feature_set = [] | |
| target_set = [] | |
| nRows = 200 | |
| randomNLimit = 2000 | |
| for i in range(0, nRows): | |
| x = random.randint(0, randomNLimit) | |
| y = random.randint(0, randomNLimit) | |
| z = random.randint(0, randomNLimit) | |
| function = (10*x)+(2*y)+(3*z) | |
| feature_set.append([x, y, z]) | |
| target_set.append(function) | |
| # Model | |
| model = LinearRegression() | |
| model.fit(feature_set, target_set) | |
| # Testing Data set | |
| test_set = [[8, 4, 7]] # Expected Output = function(8,4,7) = (10*8) + (2*4) + (3*7) = 109 | |
| prediction = model.predict(test_set) | |
| test_set_2 = [[9, 2, 2]] # Expected Output = function(9,2,2) = (10*9) + (2*2) + (3*2) = 100 | |
| prediction2 = model.predict(test_set_2) | |
| print('Prediction:' + str(prediction) + ' Co - Efficient: ' + str(model.coef_)) | |
| print('Prediction:' + str(prediction2) + ' Co - Efficient: ' + str(model.coef_)) |
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