we'll zoom in on parts of the code that might not be so evident to grasp without further explanation we'll start by explaining how the train_observation(function works this function has two large parts the first is to calculate the prediction of an observation and compare it to the actual value the second part is to change the weights if the prediction seems to be wrong the real value (yis either or the prediction is also or if it' wrong we get an error of either or - adds to error count the train observation function is run for every observation and will adjust the weights using the formula explained earlier prediction is made for this observation because it' binarythis will be either or def train_observation(self, ,yerror_count)result np dot(xself weightsself threshold error result in case we have wrong prediction (an if error ! error)we need to adjust the model error_count + for indexvalue in enumerate( )self weights[index]+=self learning_rate error value return error_count we return the error count because we need to evaluate it at the end of the epoch adjusts the weight for every predictor variable using the learning ratethe errorand the actual value of the predictor variable for every predictor variable in the input vector ( )we'll adjust its weight the prediction (yis calculated by multiplying the input vector of independent variables with their respective weights and summing up the terms (as in linear regressionthen this value is compared with the threshold if it' larger than the thresholdthe algorithm will give as outputand if it' less than the thresholdthe algorithm gives as output setting the threshold is subjective thing and depends on your business case let' say you're predicting whether someone has certain lethal diseasewith being positive and negative in this case it' better to have lower thresholdit' not as bad to be found positive and do second investigation than it is to overlook the disease and let the patient die the error is calculatedwhich will give the direction to the change of the weights result np dot(xself weightsself threshold error result the weights are changed according to the sign of the error the update is done with the learning rule for perceptrons for every weight in the weight vectoryou update its value with the following rulewi xi where wi is the amount that the weight needs to be changedis the learning rateis the errorand xi is the ith value in the input vector (the ith predictor variablethe