diff --git a/maths-prog/MachineLearningDemystified/AlgorithmsClassification.py b/maths-prog/MachineLearningDemystified/AlgorithmsClassification.py
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+directory = '/home/nuno/Documents/Jobs/IDInsight'
+
+import pandas as pd
+import numpy as np
+import joblib
+
+## Install the dataframe
+insuranceDataFrame = pd.read_csv('/home/nuno/Documents/Jobs/IDInsight/insurance_clean_continuous.csv')
+insuranceDataFrame['charges']
+## We divide our data into training and test sets, and normalize
+dfTrain = insuranceDataFrame[:1000]
+dfTest = insuranceDataFrame[1000:1300]
+dfCheck = insuranceDataFrame[1300:]
+
+means = np.mean(dfTrain, axis=0)
+stds = np.std(dfTrain, axis=0)
+
+dfTrain = (dfTrain - means) / stds
+dfTest = (dfTest - means) / stds
+dfCheck = (dfCheck - means) / stds
+
+dfTrain['charges'] = (dfTrain['charges']>=0).astype('int')
+dfTest['charges'] = (dfTest['charges']>=0).astype('int')
+dfCheck['charges'] = (dfCheck['charges']>=0).astype('int')
+
+## Convert our stuff to arrays
+trainLabel = np.asarray(dfTrain['charges'])
+trainData = np.asarray(dfTrain.drop('charges',1))
+
+testLabel = np.asarray(dfTest['charges'])
+testData = np.asarray(dfTest.drop('charges',1))
+
+## ******************************************************************
+
+## We're ready to apply the specific ML algorithms!
+
+# Naïve Bayes
+
+## Naïve Bayes: Bernoulli
+from sklearn.naive_bayes import BernoulliNB
+insuranceCheck = BernoulliNB(alpha=0.01)
+insuranceCheck.fit(trainData, trainLabel)
+
+score = insuranceCheck.score(testData, testLabel)
+print("score = ", score * 100, "/100")
+# We know that the error is R^2, we just find it more intuitive to present this as a score out of 100.
+
+algorithms = []
+algorithms = np.append(algorithms, input())
+scores = []
+scores = np.append(scores, score)
+
+## We could use the following to make predictions
+## But for the moment, we're just interested in seeing which algorithm performs best, so we'll only do this once.
+
+sampleData = dfCheck[4:5]
+sampleDataFeatures = np.asarray(sampleData.drop('charges',1))
+
+prediction = insuranceCheck.predict(sampleDataFeatures)
+print('Insurance Claim Prediction:', prediction)
+sampleData['charges']
+
+## Save the model
+joblib.dump([insuranceCheck, means, stds], directory+'/insuranceModel-NaiveBayes-Bernoulli.pkl')
+
+## This is what we would do to load the next time
+insuranceLoadedModel, means, stds = joblib.load(directory+ '/insuranceModel-NaiveBayes-Bernoulli.pkl')
+score = insuranceLoadedModel.score(testData, testLabel)
+print("score = ", score * 100,"/ 100")
+
+
+## ******************************************************************
+
+## Naïve Bayes: Gaussian
+from sklearn.naive_bayes import GaussianNB
+
+insuranceCheck = GaussianNB()
+insuranceCheck.fit(trainData, trainLabel)
+
+score = insuranceCheck.score(testData, testLabel)
+print("score = ", score * 100, "/100")
+
+algorithms = np.append(algorithms, input())
+scores = np.append(scores, score)
+
+
+## Save the model
+joblib.dump([insuranceCheck, means, stds], directory+'/insuranceModel-NaiveBayes-Gaussian.pkl')
+
+## ******************************************************************
+
+## Nearest Neighbours
+
+### We've been using the same setup for a while; we should create a function!
+
+def runAlgorithm(Classifier, algorithmName):
+    global scores, algorithms
+
+    Classifier.fit(trainData, trainLabel)
+    scoreInternal = Classifier.score(testData, testLabel)
+    print("score = ", scoreInternal * 100, "/100")
+
+    scores = np.append(scores, scoreInternal) ## These are global scope variables
+    algorithms = np.append(algorithms, algorithmName)
+
+    joblib.dump([Classifier, means, stds],
+                '/home/nuno/Documents/Jobs/IDInsight/insuranceModel-' + algorithmName + '.pkl')
+
+
+algorithmName = "NearestNeighbours"
+
+from sklearn.neighbors import KNeighborsClassifier
+
+insuranceCheck = KNeighborsClassifier(n_jobs=-1, n_neighbors=7, weights="distance", algorithm="brute", leaf_size=10, p=2)
+runAlgorithm(insuranceCheck, algorithmName)
+
+## ******************************************************************
+
+## Support Vector Machines
+from sklearn.svm import SVC
+
+algorithmName = "SVM"
+insuranceCheck = SVC(gamma='scale', kernel='poly', degree=5) # Required some tinkering
+runAlgorithm(insuranceCheck, algorithmName)
+
+## ******************************************************************
+
+## Decision Trees
+
+from sklearn.tree import DecisionTreeClassifier
+algorithmName = "Tree"
+
+### This is getting a little bit repetitive, so let's do some rudimentary hyperparameter optimization
+
+score = 0
+
+## Warning: slow
+for criterion in np.array(["gini", "entropy"]):
+    for splitter in np.array(["best", "random"]):
+        for max_depth in np.append(range(1,10), [None]):
+            print("max_depth=",max_depth)
+            for min_samples_split in (np.asarray(range(25))+2):
+                #print("min_samples_split=", min_samples_split)
+                for min_samples_leaf in (np.asarray(range(25))+1):
+                    for max_features in np.array(["log2", "auto", None]):
+                        insuranceCheck = DecisionTreeClassifier(random_state=0, criterion=criterion, splitter = splitter, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, max_features = max_features)
+                        insuranceCheck.fit(trainData, trainLabel)
+                        score_temp = insuranceCheck.score(testData, testLabel)
+                        if(score_temp > score):
+                            print("score = ", score_temp * 100, "/100")
+                            print("\nNEW BEST\ncriterion=", criterion, "\nsplitter =", splitter, "\nmax_depth =", max_depth, "\nmin_samples_split =", min_samples_split, "\nmin_samples_leaf =", min_samples_leaf, "\nmax_features =", max_features)
+                            score = score_temp
+
+insuranceCheck = DecisionTreeClassifier(random_state=0, criterion="gini", splitter = "best", max_depth = 9, min_samples_split = 15, min_samples_leaf = 4, max_features = "log2")
+
+runAlgorithm(insuranceCheck, algorithmName)
+
+## ******************************************************************
+
+## Random forests
+from sklearn.ensemble import RandomForestClassifier
+algorithmName = "RandomForest"
+
+insuranceCheck = RandomForestClassifier(n_estimators=200, criterion = "gini", min_samples_split = 14, min_samples_leaf=4, max_depth=9 , random_state=0, n_jobs=-1, max_features=None)
+
+runAlgorithm(insuranceCheck, algorithmName)
+
+## ******************************************************************
+
+## Extra random trees
+algorithmName = "ExtraRandomTrees"
+from sklearn.ensemble import ExtraTreesClassifier
+insuranceCheck = ExtraTreesClassifier(n_estimators=300, max_depth=None)
+runAlgorithm(insuranceCheck, algorithmName)
+
+## ******************************************************************
+
+## MultiLayerPerceptron (NN)
+algorithmName = "MultiLayerPerceptron"
+from sklearn.neural_network import MLPClassifier
+insuranceCheck = MLPClassifier(max_iter=800, hidden_layer_sizes=150)
+runAlgorithm(insuranceCheck, algorithmName)
+
+## ******************************************************************
+
+for i in range(len(scores)):
+    print(algorithms[i], ": ", scores[i])
+
+## And that's it. We notice that most of our algorithms do pretty well, with a score of .89 - .93, where the maximum is 1.
+## This means that a lot of the variability in the sample is extracted by our model.
+