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+directory = '/home/nuno/Documents/Jobs/IDInsight'
+
+import pandas as pd
+import numpy as np
+import joblib
+import matplotlib
+
+
+## Install the dataframe
+insuranceDataFrame = pd.read_csv(directory +'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
+
+## We don't want this part, only when classifying:
+## 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 reuse the old 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],
+                directory +'insuranceModel-' + algorithmName + '.pkl')
+
+algorithms = []
+scores = []
+
+## And we are ready!
+
+## ******************************************************************
+
+## Linear Regression
+algorithmName = "LinearRegression"
+from sklearn.linear_model import LinearRegression
+insuranceCheck = LinearRegression()
+runAlgorithm(insuranceCheck, algorithmName)
+
+### We also want to visualize some stuff this time:
+
+coeff = list(insuranceCheck.coef_)
+labels = list(dfTrain.drop('charges',1).columns)
+features = pd.DataFrame()
+features['Features'] = labels
+features['importance'] = coeff
+features.sort_values(by=['importance'], ascending=True, inplace=True)
+features['positive'] = features['importance'] > 0
+features.set_index('Features', inplace=True)
+features.importance.plot(kind='barh', figsize=(11, 6),color = features.positive.map({True: 'blue', False: 'red'}))
+
+## ******************************************************************
+
+## Lasso
+algorithmName = "Lasso"
+from sklearn.linear_model import LassoCV
+insuranceCheck = LassoCV(cv=5, n_alphas=20, tol=0.0001, n_jobs=-1)
+runAlgorithm(insuranceCheck, algorithmName)
+
+## ******************************************************************
+
+## Nearest Neighbours Regression
+algorithmName = "NearestNeighboursRegression"
+
+from sklearn.neighbors import KNeighborsRegressor
+
+### Let's do some hyperparameter tuning:
+score=0
+for n_neighbors in range(1,100):
+    for leaf_size in range(1,100):
+        insuranceCheck = KNeighborsRegressor(n_jobs=-1, n_neighbors=n_neighbors, weights="distance", algorithm="brute", leaf_size=leaf_size, p=2)
+        insuranceCheck.fit(trainData, trainLabel)
+
+        score_temp = insuranceCheck.score(testData, testLabel)
+
+        if(score_temp >score):
+            print("\nscore = ", score_temp * 100, "/100")
+            print("n_neighbors = ", n_neighbors)
+            print("leaf_size = ", leaf_size)
+            score=score_temp
+
+insuranceCheck = KNeighborsRegressor(n_jobs=-1, n_neighbors=8, weights="distance", algorithm="brute", leaf_size=1, p=2)
+runAlgorithm(insuranceCheck, algorithmName)
+
+## ******************************************************************
+
+## Linear SVR
+algorithmName = "LinearSVR"
+from sklearn.svm import LinearSVR
+
+n=4000
+insuranceCheck = LinearSVR(max_iter=n)
+runAlgorithm(insuranceCheck, algorithmName)
+
+## ******************************************************************
+
+## SVR different kernels.
+algorithmName = "SVR_RBF"
+from sklearn.svm import SVR
+insuranceCheck = SVR(gamma='auto', C=1, epsilon=0.2, kernel='rbf')
+    ## There are different kernels available, including polynomial.
+    ## After some tinkering, RBF seems to be the best
+runAlgorithm(insuranceCheck, algorithmName)
+
+## ******************************************************************
+
+## Tree regression.
+algorithmName = "TreeRegression"
+from sklearn.tree import DecisionTreeRegressor
+
+## Hyper parameter optimization.
+score = 0
+
+for criterion in np.array(["mse", "friedman_mse", "mae"]):
+    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 = DecisionTreeRegressor(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 = DecisionTreeRegressor(random_state=0, criterion="mse", splitter = "random", min_samples_split=14, min_samples_leaf=4, max_features = "auto", max_depth= 9)
+runAlgorithm(insuranceCheck, algorithmName)
+
+## ******************************************************************
+
+## Random forest regression.
+algorithmName = "RandomForestRegression"
+
+from sklearn.ensemble import RandomForestRegressor
+insuranceCheck = RandomForestRegressor(n_estimators=500, max_depth=None, random_state=0, n_jobs=-1)
+runAlgorithm(insuranceCheck, algorithmName)
+
+## ******************************************************************
+
+## Extra random forest regression.
+algorithmName = "ExtraRandomTreesRegression"
+from sklearn.ensemble import ExtraTreesRegressor
+insuranceCheck = ExtraTreesRegressor(n_estimators=500, max_depth=None, min_samples_split=20, min_samples_leaf=20, n_jobs=-1)
+runAlgorithm(insuranceCheck, algorithmName)
+
+## ******************************************************************
+## MultiLayerPerceptron Regression(NN)
+
+from sklearn.neural_network import MLPRegressor
+algorithmName = "MLP_Regresion"
+
+for randomstate in range(10):
+    insuranceCheck = MLPRegressor(max_iter=1200, hidden_layer_sizes=(30,30,30), random_state=randomstate, learning_rate="adaptive", activation="relu")
+    insuranceCheck.fit(trainData, trainLabel)
+    score = insuranceCheck.score(testData, testLabel)
+    print("score = ", score * 100, "/100")
+
+insuranceCheck = MLPRegressor(max_iter=1200, hidden_layer_sizes=(30,30,30), random_state=5, learning_rate="adaptive", activation="relu")
+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, hovering around .81. Dishonorable mention to LinearSVR.
+
+## Overall, Trees do pretty great.