def runRegression(purchases):
# define the model
lrModel = LinearRegression()
# dummy training to initialize weights and biases of the model
lrModel.fit(np.array([0]).reshape(-1, 1), [1])
# Assigning trained weights and biases to the model
lrModel.coef_ = np.array([[0.08037347]]) # weights
lrModel.bias_ = np.array([3.68091473]) # bias
example_instance = np.array([purchases
]).reshape(-1, 1) # [NUMBER OF PURCHASES]
# Test the model
prediction = lrModel.predict(example_instance)
print(prediction)
return prediction[0].item()
