#cassignment

[444names]

french forenames + norse deities + the entire wikipedia article on "magic" in french

Absopon Absort Acennes Aclée Acquaintio Actictoure Actue Adapha Agmedié Agríðr Ailique Aingra Aistée Alicans Alpte Amérole Analieux Angers Angle Anguðr Annadia Anosit Anosouiter Antasa Appatil Appeillit Appre Arine Armand Armécrie Aroure Assine Athristion Ation Aurephez Auseur Averre Aéraine Aéritues Balberne Besuente Bibierte Bilint Bille Bligneme Braliguš Brete Brite Bropérot Buches Buffetter Caecus Cassigne Cathé Catielle Catte Celle Cence Cencturs Cermuent Cessaxnōt Chand Channettes Chaïques Chercels Chergie Cheurs Chiéth Chodit Chosmente Chovainter Cince Cinges Claistic Clementése Comal Commaut Comonne Comproppe Conce Condide Conhose Conifect Conne Connermóðr Connes Connées Conté Corage Coragient Cordouirir Corieux Corment Courvée Coustron Criblint Cédoxemes Dagis Danculions Davoir Dayenom Daymblaux Demières Destaine Dialent Diciés Dictice Dienve Difire Dikhaur Dinvois Diques Dist-Maité Doure Doximpe Défauveure Dépicules Déside Dévinitue Dévoques Eaurs Eficielle Eflun Elinent Ellées Emences Emene Enfée Enote Enrise Entel Entraitur Exciés Exiend Exigènel Explieus Explit Fable Fairombe Fanès Favre Femplut Ficiées Flancilisa Flatié Flogeant Foirces Fonalision Fonge Fonne Fordigueux Forthon Forya Fréhillé Gamnist Gatur Geaurertee Geircheux Gentur Georiets Giablitudd Giamés Goncouvé Googique Guels Guïté Göldre Haratia Hemerna Henfique Hientibe Hinfam Hoducient Hugur Huitient Hériteurs Ichorts Igiques Imdanussa Impast Incelf Incition Indales Inelle Inent Inervempan Infonsiry Inite Innétoutas Insion Invend Inveures Irecquale Iseme Jacturbre Jasité Jeaniques Jettans Jettes Jeusse Joient Joujoël Jérène Kableme Kabres Kurgies Laffigie Lation LaVeres Letter Lient Lieur Lions Liontesque Litépulan Logiqueste Lorranche Luirce Lumernaler Lumise Léviseux Lévismerry Magante Magiciense Magient Magisent Magnomment Maine Majouble Malbisigus Mandes Mangeaur Mantion Marces Marchris Marme Marrubles Marte Marthie Martinaté Martinces Mation Mationdes Matudiver Merce Mique Mitaients Modivire Mogué Moinwares Moisme Monir Monsent Myrissont Mysients Myssions Médant Médiane Médie Méfilique Mélisers Métionort Mêment Nadenne Nanannique Nocture Noirel Nolanne Noson Nuelgaté Négent Oberre Obscres Occie Occès Ocint Oloume Opelquels Ophisław Orerne Orianaité Ortendre Orthire Outeusque Outéante Panum Pançans Pappétill Pardaveura Parmages Patoubes Pelodign Perndesse Pespos Petter Phove Pient Planu Pliairies Plies Plisanuels Pocon Potes Pourise Prent Preques Prichangle Priergne Pritérie Proder Prommes Prompa Prévère Puyencerre Quelque Ragiernes Raismes Rannamede Raturs Ravantes Ravau Razismenre Reirempare Renchens Rettez Risavégyps Rition Rogiert Romadivie Rugorition Ráðguš Récis Rédin Rémer Rétiont Saade Sagion Sainales Saitus Saité Sance Sandge Sarundée Saystel Scaté Sibles Sibénés Sidain Sidonisme Sieus Sihrogin Sique Siques Sièment Smien Sologa Solorment Somaux Sonstra Soposévent Sorgeor Sparchaux Speres Sphanu Standríðr Steme Stitiatue Strair Sualheux Suberne Subersque Sureina Surel Surgin Surgismus Swedent Swedgrie Sychèle Symbo Syrið Szarent Séest Sépére Taigie Tailà Thant Thissien Thriété Thées Thétérie Traldres Tranisable Trations Tretoupe Trieux Trohamique Trotionne Troviss Troyers Trôle Unifint Uniment Unipers Unolign Ut-ille Vairion Vanise Vicle Vidif Viente Viges Vignété Vilerre Viste Vitues Viver Voireme Volationne Volumide Volustes Válivell Végypte Vénosée Wilès Wortage Xercentes Yautibiama Yosée Yvoire Écadassan Écentes Écieurers Édiets Élismentes Émieux Époses Étabre Étinen Étuelle Étues Évent Évoqueires Êtrifient Êtrouirs

[assignmenthelp-em]

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[machine-learning-for-data-blog]

Week 4: K-means Cluster Analysis

A k-means cluster analysis was conducted to identify underlying subgroups of internet user rate based on their similarity of responses on 9 variables that represent characteristics that could have an impact. All clustering variables were standardized to have a mean of 0 and a standard deviation of 1. 

Data were randomly split into a training set that included 70% of the observations and a test set that included 30% of the observations. 

ctrain, ctest = train_test_split(predictors, test_size = 0.3, random_state=123)

A series of k-means cluster analyses were conducted on the training data specifying k=1-9 clusters, using Euclidean distance. 

clusters = range(1,10) mindistance = list()

for val in clusters:    model = KMeans(n_clusters = val)    model.fit(ctrain)    cassign = model.predict(ctrain)    mindistance.append(sum(np.min(cdist(ctrain, model.cluster_centers_, 'euclidean'), axis=1)) / ctrain.shape[0])

The variance in the clustering variables that was accounted for by the clusters (r-square) was plotted for each of the nine cluster solutions in an elbow curve to provide guidance for choosing the number of clusters to interpret. 

The elbow curve was inconclusive, suggesting that the 2, 4 and 5-cluster solutions might be interpreted. The results below are for an interpretation of the 4-cluster solution.

model3 = KMeans(n_clusters=4) model3.fit(ctrain) cassign = model3.predict(ctrain)

Canonical discriminant analyses was used to reduce the clustering variable down a few variables that accounted for most of the variance in the clustering variables. Observations in cluster one(yellow) were spread out more than the other clusters. internet user rate in cluster zero(green) had the highest clustering variable where cluster three(violet) have moderate variable.

ctrain.reset_index(level=0, inplace=True)

cluslist = list(ctrain['index'])

labels = list(model3.labels_) newlist = dict(zip(cluslist, labels)) print(newlist)

newclus = pd.DataFrame.from_dict(newlist, orient='index') newclus.columns = ['cluster'] newclus.reset_index(level=0, inplace=True) merged_train = pd.merge(ctrain, newclus, on='index') merged_train.head(n=10)

Split internetuserate data into train and test sets 

internetuserate_data = data['internetuserate'] internetuserate_train, internetuserate_test = train_test_split(internetuserate_data, test_size=.3, random_state=123) internetuserate_train1 = pd.DataFrame(internetuserate_train) internetuserate_train1.reset_index(level=0, inplace=True) merged_train_all=pd.merge(internetuserate_train1, merged_train, on='index') sub5 = merged_train_all[['internetuserate', 'cluster']].dropna()

In order to externally validate the clusters, an Analysis of Variance was conducting to test for significant differences between the clusters on grade point average (GPA). Internet user rate in cluster 0 had the highest GPA, and cluster 1 had the lowest GPA. 

[speedessays]

Compare the relative costs of lawn care at each of three locations; the hospital itself, the outpatient center, and the hospital-affiliated nursing home down the block.

Compare the relative costs of lawn care at each of three locations; the hospital itself, the outpatient center, and the hospital-affiliated nursing home down the block.

Compare the relative costs of lawn care at each of three locations; the hospital itself, the outpatient center, and the hospital-affiliated nursing home down the block.

HSA 525 EXERCISE14-4

CAssignment Exercise 14-4: The Discovery The Chief FinancialOfficer at Sample Hospital has just discovered that the hospital’sChief of the Medical Staff’s son Jason, a student at the localcommunity college, is…

[nursingtermpapers]

Compare the relative costs of lawn care at each of three locations; the hospital itself, the outpatient center, and the hospital-affiliated nursing home down the block.

Compare the relative costs of lawn care at each of three locations; the hospital itself, the outpatient center, and the hospital-affiliated nursing home down the block.

HSA 525 EXERCISE14-4

CAssignment Exercise 14-4: The Discovery The Chief FinancialOfficer at Sample Hospital has just discovered that the hospital’sChief of the Medical Staff’s son Jason, a student at the localcommunity college, is…

[mrs--admin-m]

Today's Assignment [College Students]

Alright guys, for your first senior assignment you’ll have to write an essay. 4 paragraphs long. You’ll write about lessons you’ve learned during your high school experience. I expect effort because the is worth 50% of your first semester grade. This is what I expect in each paragraph:

Paragraph 1: Short Intro

Paragraph 2: A lesson you learned

Paragraph 3: Another lesson you learned

Paragraph 4: Conclusion

This assignment is due by Monday which means you have 6 days. I expect it in on time. I will be watching.

[nursingtermpapers]

Compare the relative costs of lawn care at each of three locations; the hospital itself, the outpatient center, and the hospital-affiliated nursing home down the block.

Compare the relative costs of lawn care at each of three locations; the hospital itself, the outpatient center, and the hospital-affiliated nursing home down the block.

HSA 525 EXERCISE14-4

CAssignment Exercise 14-4: The Discovery The Chief FinancialOfficer at Sample Hospital has just discovered that the hospital’sChief of the Medical Staff’s son Jason, a student at the localcommunity college, is…