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def predict(x, y): ex = sum(x) / len(x) ey = sum(y) / len(y) numerator = sum([(xi - ex) * (yi - ey) for xi, yi in zip(x, y)]) denominator = sum([(xi - ex)**2 for xi in x]) slope = numerator / denominator intercept = ey - (slope * ex) predict = slope * 10 + intercept return predict if __name__ == '__main__': feature_1 = "Physics Scores 15 12 8 8 7 7 7 6 5 3" feature_2 = "History Scores 10 25 17 11 13 17 20 13 9 15" feature_1 = [int(x) for x in feature_1.split() if x.isnumeric()] feature_2 = [int(x) for x in feature_2.split() if x.isnumeric()] result = predict(feature_1, feature_2) print(f'{result:.3f}')
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Correlation and Regression Lines - A quick recap #3
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