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support_vector_machine.py # Support Vector Machine (SVM) # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.linear_model import...

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support_vector_machine.py
# Support Vector Machine (SVM)
# Importing the li
aries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.linear_model import LinearRegression
# Importing the dataset
# Importing the dataset
dataset = pd.read_csv(r'C:\Users\David\OneDrive - Savannah State University\VisitJamaica_final.csv',sep="~")
X = dataset.iloc[:, [0, 1]].values
y = dataset.iloc[:, 2].values
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
print(X_train)
print(y_train)
print(X_test)
print(y_test)
# Feature Scaling
from sklearn.preprocessing import StandardScale
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
print(X_train)
print(X_test)
# Training the SVM model on the Training set
from sklearn.svm import SVC
classifier = SVC(kernel = 'linear', random_state = 0)
classifier.fit(X_train, y_train)
# Predicting a new result
# Predicting the Test set results
y_pred = classifier.predict(X_test)
print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score = accuracy_score(y_test, y_pred)
#Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = sc.inverse_transform(X_train), y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 5, stop = X_set[:, 0].max() + 5, step = 0.25),
XXXXXXXXXXnp.arange(start = X_set[:, 1].min() - 5, stop = X_set[:, 1].max() + 5, step = 0.25))
plt.contourf(X1, X2, classifier.predict(sc.transform(np.a
ay([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),
XXXXXXXXXXalpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('SVM (Training set)')
plt.xlabel('')
plt.ylabel('')
plt.legend()
plt.show()
#Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = sc.inverse_transform(X_test), y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() + 0, stop = X_set[:, 0].max() + 0, step = 0.25),
XXXXXXXXXXnp.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.25))
plt.contourf(X1, X2, classifier.predict(sc.transform(np.a
ay([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),
XXXXXXXXXXalpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('SVM (Test set)')
plt.xlabel('')
plt.ylabel('')
plt.legend()
plt.show()
decision_tree_regression.py
# Decision Tree Regression
# Importing the li
aries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv(r'C:\Users\David\OneDrive - Savannah State University\VisitJamaica_final.csv',sep="~")
X = dataset.iloc[:, 0].values
y = dataset.iloc[:, 1].values
X = X.reshape(-1,1)
y = y.reshape(-1,1)
# Training the Decision Tree Regression model on the whole dataset
from sklearn.tree import DecisionTreeRegresso
egressor = DecisionTreeRegressor(random_state = 0)
egressor.fit(X, y)
# Predicting a new result
egressor.predict([[60]])
# Visualising the Decision Tree Regression results (higher resolution)
X_grid = np.arange(min(X), max(X), 0.01)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color = 'red')
plt.plot(X_grid, regressor.predict(X_grid), color = 'green')
plt.title(' (Decision Tree Regression)')
plt.xlabel('')
plt.ylabel('')
plt.show()
VisitJamaica_today.csv
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215~28429~43~0~3~Male~2~0~59~60~0~1~5~1
216~28429~43~0~3~Male~2~0~59~60~0~1~5~1
217~28429~43~0~3~Male~2~0~59~60~0~1~5~1
218~28429~43~0~3~24789~2~0~59~5~60~0~1~1
219~79362~43~1~2~Female~4~0~50~45~1~2~4~1
220~79362~43~1~2~Female~4~0~50~45~1~2~4~1
221~79362~43~1~2~Female~4~0~50~45~1~2~4~1
222~79362~43~1~2~Female~4~0~50~45~1~2~4~1
223~46483~43~1~0~Male~3~1~47~41~1~2~4~0
224~46483~43~1~0~Male~3~1~47~41~1~1~4~0
225~46483~43~1~0~Male~3~1~47~41~1~2~4~0
226~46483~43~1~0~Male~3~1~47~41~1~2~4~0
227~82438~44~1~4~Female~1~1~51~50~0~2~5~1
228~82438~44~1~4~Female~1~1~51~50~0~2~5~1
229~82438~44~1~4~Female~1~1~51~50~0~2~5~1
230~82438~44~1~4~Female~1~1~51~50~0~2~5~1
231~82335~44~0~4~Male~2~0~69~46~0~1~7~1
232~82335~44~0~4~Male~2~0~69~46~0~1~7~1
233~82335~44~0~4~Male~2~0~69~46~0~1~7~1
234~82335~44~0~4~Male~2~0~69~46~0~1~7~1
235~63265~46~0~0~Female~1~0~27~51~1~0~1~0
236~63265~46~0~0~Female~1~0~27~51~1~0~1~0
237~63265~46~0~0~Female~1~0~27~51~1~0~1~0
238~63265~46~0~0~Female~1~0~27~51~1~0~1~0
239~70167~46~0~3~Male~3~0~53~46~1~1~5~1
240~70167~46~0~3~Male~3~0~53~46~1~1~5~1
241~70167~46~0~3~Male~3~0~53~46~1~1~5~1
242~70167~46~0~3~Male~3~0~53~46~1~1~5~1
243~50066~46~0~3~Male~1~0~70~56~1~1~7~1
244~50066~46~0~3~Male~1~0~70~56~1~1~7~1
245~50066~46~0~3~Male~1~0~70~56~1~1~7~1
246~50066~46~0~3~Male~1~0~70~56~1~1~7~1
247~63314~46~0~2~Male~2~0~19~55~1~0~0~0
248~63314~46~0~2~Male~2~0~19~55~1~0~0~0
249~63314~46~0~2~Male~2~0~19~55~1~0~0~0
250~63314~46~0~2~Male~2~0~19~55~1~0~0~0
251~63314~46~~2~Male~2~0~19~0~~1~0~1
252~20319~47~0~0~Female~2~0~67~52~1~1~7~1
253~20319~47~0~0~Female~2~0~67~52~1~1~7~1
254~20319~47~0~0~Female~2~0~67~52~1~1~7~1
255~20319~47~0~0~Female~2~0~67~52~1~1~7~1
256~17311~47~1~2~Female~2~1~54~59~1~2~5~1
257~17311~47~1~2~Female~2~1~54~59~1~2~5~0
258~17311~47~1~2~Female~2~1~54~59~1~2~5~1
259~17311~47~1~2~Female~2~1~54~59~1~2~5~1
260~59198~48~0~4~Male~1~0~63~51~1~1~5~1
261~59198~48~0~4~Male~1~0~63~51~1~1~5~1
262~59198~48~0~4~Male~1~0~63~51~1~1~5~1
263~59198~48~0~4~Male~1~0~63~51~1~1~5~1
264~69762~48~1~3~Male~2~0~18~59~0~2~0~0
265~69762~48~1~3~Male~2~0~18~59~0~1~0~0
266~69762~48~1~3~Male~2~0~18~59~0~1~0~0
267~69762~48~1~3~Male~2~0~18~59~0~1~0~0
268~69762~48~1~3~Male~three~0~18~0~59~0~1~0
269~41128~48~0~1~Female~2~0~43~50~0~1~4~0
270~41128~48~0~1~Female~2~0~43~50~0~1~4~0
271~41128~48~0~1~Female~2~0~43~50~0~1~4~0
272~41128~48~0~1~Female~2~0~43~50~0~1~4~0
273~22911~48~0~2~Female~4~1~68~48~0~1~7~1
274~22911~48~0~2~Female~4~1~68~48~0~1~7~1
275~22911~48~0~2~Female~4~1~68~48~0~1~7~1
276~22911~48~0~2~Female~4~1~68~48~0~1~7~1
277~74627~48~0~3~Male~4~0~19~59~0~0~0~0
278~74627~48~0~3~Male~4~0~19~59~0~0~0~0
279~74627~48~0~3~Male~4~0~19~59~0~0~0~0
280~74627~48~0~3~Male~4~0~19~59~0~0~0~0
281~74005~48~1~3~Female~4~0~32~47~1~2~2~0
282~74005~48~1~3~Female~4~0~32~47~1~2~2~0
283~74005~48~1~3~Female~4~0~32~47~1~0~2~0
284~74005~48~1~3~Female~4~0~32~47~1~2~2~0
285~19166~49~1~0~Male~4~0~70~55~0~2~7~1
286~19166~49~1~0~Male~4~0~70~55~0~2~7~1
287~19166~49~1~0~Male~4~0~70~55~0~2~7~1
288~19166~49~1~0~Male~4~0~70~55~0~2~7~1
289~47142~49~1~4~Female~2~0~47~42~1~2~4~0
290~47142~49~1~4~Female~2~0~47~42~1~2~4~0
291~47142~49~1~4~Female~2~0~47~42~1~2~4~0
292~47142~49~1~4~Female~2~0~47~42~1~2~4~0
293~24530~50~1~0~Female~1~0~60~49~0~2~5~1
294~24530~50~1~0~Female~1~0~60~49~0~2~5~1
295~24530~50~1~0~Female~1~0~60~49~0~2~5~1
296~24530~50~1~0~Female~1~0~60~49~0~2~5~1
297~70677~50~0~3~Female~4~0~60~56~1~1~5~1
298~70677~50~0~3~Female~4~0~60~56~1~1~5~1
299~70677~50~0~3~Female~4~0~60~56~1~1~5~1
300~70677~50~0~3~Female~4~0~60~56~1~1~5~1
301~42311~54~1~3~Male~4~1~59~47~0~2~5~1
302~42311~54~1~3~Male~4~1~59~47~0~2~5~1
303~42311~54~1~3~Male~4~1~59~47~0~2~5~0
304~42311~54~1~3~Male~4~1~59~47~0~2~5~1
305~36222~54~0~0~Male~1~0~26~54~1~0~1~0
306~36222~54~0~0~Male~1~0~26~54~1~0~1~0
307~36222~54~0~0~Male~1~0~26~54~1~0~1~0
308~36222~54~0~0~Male~1~0~26~54~1~0~1~0
309~39119~54~1~4~Female~4~0~45~53~0~2~4~1
310~39119~54~1~4~Female~4~0~45~53~0~2~4~1
311~39119~54~1~4~Female~4~0~45~53~0~2~4~1
312~39119~54~1~4~Female~4~0~45~53~0~2~4~1
313~58065~54~1~2~Male~1~0~40~48~0~2~2~0
314~58065~54~1~2~Male~1~0~40~48~0~2~2~0
315~58065~54~1~2~Male~1~0~40~48~0~2~2~0
316~58065~54~1~2~Male~1~0~40~48~0~2~2~0
317~13270~54~1~1~Female~2~1~49~42~1~2~4~1
318~13270~54~1~1~Female~2~1~49~42~1~2~4~1
319~13270~54~1~1~Female~2~1~49~42~1~2~4~1
320~13270~54~1~1~Female~2~1~49~42~1~2~4~1
321~13749~54~1~0~Male~3~0~57~51~0~2~5~1
322~13749~54~1~0~Male~3~0~57~51~0~2~5~1
323~13749~54~1~0~Male~3~0~57~51~0~2~5~1
324~13749~54~1~0~Male~3~0~57~51~0~2~5~1
325~59311~54~1~0~Male~3~0~38~55~0~1~2~0
326~59311~54~1~0~Male~3~0~38~55~0~2~2~0
327~59311~54~1~0~Male~3~0~38~55~0~2~2~0
328~59311~54~1~0~Male~3~0~38~55~0~2~2~0
329~49753~54~0~3~Male~3~0~67~41~0~1~7~1
330~49753~54~0~3~Male~3~0~67~41~0~1~7~1
331~49753~54~0~3~Male~3~0~67~41~0~1~7~1
332~49753~54~0~3~Male~3~0~67~41~0~1~7~1
333~72628~54~0~4~Female~1~1~46~44~0~1~4~0
334~72628~54~0~4~Female~1~1~46~44~0~1~4~0
335~72628~54~0~4~Female~1~1~46~44~0~1~4~0
336~72628~54~0~4~Female~1~1~46~44~0~1~4~0
337~34798~54~1~4~Female~2~1~21~57~0~2~0~0
338~34798~54~1~4~Female~2~1~21~57~0~1~0~0
339~34798~54~1~4~Female~2~1~21~57~0~1~0~0
340~34798~54~1~4~Female~2~1~21~57~0~1~0~0
341~11286~54~0~3~Male~2~0~48~46~1~1~4~1
342~11286~54~0~3~Male~2~0~48~46~1~1~4~1
343~11286~54~0~3~Male~2~0~48~46~1~1~4~1
344~11286~54~0~3~Male~2~0~48~46~1~1~4~1
345~50432~57~0~3~Female~2~0~55~58~1~1~5~1
346~50432~57~0~3~Female~2~0~55~58~1~1~5~1
347~50432~57~0~3~Female~2~0~55~58~1~1~5~1
348~50432~57~0~3~Female~2~0~55~58~1~1~5~1
349~71314~57~1~3~Female~4~0~22~55~1~2~0~0
350~71314~57~1~3~Female~4~0~22~55~1~2~0~0
351~71314~57~1~3~Female~4~0~22~55~1~2~0~0
352~71314~57~1~3~Female~4~0~22~55~1~2~0~1
353~24686~58~0~0~Female~3~0~34~60~0~0~2~0
354~24686~58~0~0~Female~3~0~34~60~0~0~2~0
355~24686~58~0~0~Female~3~0~34~60~0~0~2~0
356~24686~58~0~0~Female~3~0~34~60~0~0~2~0
357~55532~58~0~1~Female~3~1~50~46~0~1~4~1
358~55532~58~0~1~Female~3~1~50~46~0~1~4~1
359~55532~58~0~1~Female~3~1~50~46~0~1~4~1
360~55532~58~0~1~Female~3~1~50~46~0~1~4~1
361~22671~59~0~0~Female~3~1~68~55~0~1~7~1
362~22671~59~0~0~Female~3~1~68~55~0~1~7~1
363~22671~59~0~0~Female~3~1~68~55~0~1~7~1
364~22671~59~0~0~Female~3~1~68~55~0~0~7~1
365~63493~59~0~3~Male~1~0~18~41~0~0~0~0
366~63493~59~0~3~Male~1~0~18~41~0~0~0~0
367~63493~59~0~3~Male~1~0~18~41~0~0~0~0
368~63493~59~0~3~Male~1~0~18~41~0~0~0~0
369~50060~60~1~1~Male~4~0~48~49~0~2~4~1
370~50060~60~1~1~Male~4~0~48~49~0~2~4~1
371~50060~60~1~1~Male~4~0~48~49~0~2~4~1
372~50060~60~1~1~Male~4~0~48~49~0~2~4~1
373~54000~60~0~1~Female~4~1~40~40~1~1~2~0
374~54000~60~0~1~Female~4~1~40~40~1~1~2~0
375~54000~60~0~1~Female~4~1~40~40~1~1~2~0
376~54000~60~0~1~Female~4~1~40~40~1~1~2~0
377~24781~60~1~3~Female~1~1~32~42~0~2~2~0
378~24781~60~1~3~Female~1~1~32~42~0~2~2~0
379~24781~60~1~3~Female~1~1~32~42~0~2~2~0
380~24781~60~1~3~Female~1~1~32~42~0~2~2~0
381~28469~60~0~1~Male~1~1~24~52~0~0~0~0
382~28469~60~0~1~Male~1~1~24~52~0~0~0~0
383~28469~60~0~1~Male~1~1~24~52~0~0~0~0
384~28469~60~0~1~Male~1~1~24~52~0~0~0~0
385~54408~60~1~2~Female~2~0~47~47~1~2~4~1
386~54408~60~1~2~Female~2~0~47~47~1~2~4~1
387~54408~60~1~2~Female~2~0~47~47~1~2~4~1
388~54408~60~1~2~Female~2~0~47~47~1~2~4~1
389~24994~60~0~1~Female~2~0~27~50~1~0~1~0
390~24994~60~0~1~Female~2~0~27~50~1~0~1~0
391~24994~60~0~1~Female~2~0~27~50~1~0~1~0
392~24994~60~0~1~Female~2~0~27~50~1~0~1~0
393~59729~61~0~2~Male~1~0~48~42~1~1~4~0
394~59729~61~0~2~Male~1~0~48~42~1~1~4~0
395~59729~61~0~2~Male~1~0~48~42~1~1~4~0
396~59729~61~0~2~Male~1~0~48~42~1~1~4~0
397~37415~61~1~3~Male~4~0~20~49~1~1~0~0
398~37415~61~1~3~Male~4~0~20~49~1~1~0~0
399~37415~61~1~3~Male~4~0~20~49~1~1~0~0
400~37415~61~1~3~Male~4~0~20~49~1~1~0~0
401~37415~61~1~3~Male~4~0~20~0~forty nine~1~0~1
402~41610~62~0~3~Female~4~1~49~48~0~1~4~1
403~41610~62~0~3~Female~4~1~49~48~0~1~4~1
404~41610~62~0~3~Female~4~1~49~48~0~1~4~1
405~41610~62~0~3~Female~4~1~49~48~0~1~4~1
406~13631~62~1~3~Male~1~0~67~59~1~2~7~1
407~13631~62~1~3~Male~1~0~67~59~1~2~7~1
408~13631~62~1~3~Male~1~0~67~59~1~2~7~1
409~13631~62~1~3~Male~1~0~67~59~1~2~7~1
410~83691~62~1~4~Male~3~0~26~55~0~2~1~0
411~83691~62~1~4~Male~3~0~26~55~0~2~1~0
412~83691~62~1~4~Male~3~0~26~55~0~2~1~0
413~83691~62~1~4~Male~3~0~26~55~0~2~1~0
414~24256~62~0~1~Male~1~1~49~56~1~1~4~1
415~24256~62~0~1~Male~1~1~49~56~1~1~4~1
416~24256~62~0~1~Male~1~1~49~56~1~1~4~1
417~24256~62~0~1~Male~1~1~49~56~1~1~4~1
418~73576~62~1~3~Female~4~0~21~42~0~1~0~0
419~73576~62~1~3~Female~4~0~21~42~0~1~0~0
420~73576~62~1~3~Female~4~0~21~42~0~1~0~0
421~73576~62~1~3~Female~4~0~21~42~0~1~0~0
422~41776~63~0~4~Female~3~0~66~50~1~1~7~1
423~41776~63~0~4~Female~3~0~66~50~1~1~7~1
424~41776~63~0~4~Female~3~0~66~50~1~1~7~1
425~41776~63~0~4~Female~3~0~66~50~1~1~7~1
426~22742~63~1~1~Male~3~0~54~46~1~2~5~1
427~22742~63~1~1~Male~3~0~54~46~1~2~5~1
428~22742~63~1~1~Male~3~0~54~46~1~2~5~1
429~22742~63~1~1~Male~3~0~54~46~1~2~5~1
430~75770~63~1~1~Male~2~0~68~43~0~2~7~1
431~75770~63~1~1~Male~2~0~68~43~0~2~7~1
432~75770~63~1~1~Male~2~0~68~43~0~2~7~1
433~75770~63~1~1~Male~2~0~68~43~0~2~7~1
434~71088~63~0~2~Male~3~0~66~48~0~1~7~1
435~71088~63~0~2~Male~3~0~66~48~0~1~7~1
436~71088~63~0~2~Male~3~0~66~48~0~1~7~1
437~71088~63~0~2~Male~3~0~66~48~0~1~7~1
438~41839~63~1~2~Male~4~1~65~52~1~2~5~1
439~41839~63~1~2~Male~4~1~65~52~1~2~5~1
440~41839~63~1~2~Male~4~1~65~52~1~2~5~1
441~41839~63~1~2~Male~4~1~65~52~1~2~5~1
442~20613~63~0~1~Female~3~1~19~54~0~0~0~0
443~20613~63~0~1~Female~3~1~19~54~0~0~0~0
444~20613~63~0~1~Female~3~1~19~54~0~0~0~0
445~20613~63~0~1~Female~3~1~19~54~0~0~0~0
446~62751~64~0~1~Female~2~0~38~42~1~1~2~0
447~62751~64~0~1~Female~2~0~38~42~1~1~2~0
448~62751~64~0~1~Female~2~0~38~42~1~1~2~0
449~62751~64~0~1~Female~2~0~38~42~1~1~2~0
450~42285~64~1~0~Male~4~1~19~46~1~1~0~0
451~42285~64~1~0~Male~4~1~19~46~1~1~0~0
452~42285~64~1~0~Male~4~1~19~46~1~1~0~0
453~42285~64~1~0~Male~4~1~19~46~1~1~0~0
454~21684~65~1~0~Female~1~1~18~48~0~1~0~0
455~21684~65~1~0~Female~1~1~18~48~0~1~0~0
456~21684~65~1~0~Female~1~1~18~48~0~1~0~0
457~21684~65~1~0~Female~1~1~18~48~0~1~0~0
458~40197~65~0~2~Female~4~1~19~50~1~0~0~0
459~40197~65~0~2~Female~4~1~19~50~1~0~0~0
460~40197~65~0~2~Female~4~1~19~50~1~0~0~0
461~40197~65~0~2~Female~4~1~19~50~1~0~0~0
462~~65~0~2~Female~~1~19~0~50~~1~0
463~17686~65~1~2~Female~4~1~63~43~0~2~5~1
464~17686~65~1~2~Female~4~1~63~43~0~2~5~1
465~17686~65~1~2~Female~4~1~63~43~0~2~5~1
466~17686~65~1~2~Female~4~1~63~43~0~2~5~1
467~41364~65~1~3~Female~1~1~49~59~0~2~4~1
468~41364~65~1~3~Female~1~1~49~59~0~2~4~1
469~41364~65~1~3~Female~1~1~49~59~0~2~4~1
470~41364~65~1~3~Female~1~1~49~59~0~2~4~1
471~41140~67~1~4~Female~1~1~51~43~0~2~5~1
472~41140~67~1~4~Female~1~1~51~43~0~2~5~1
473~41140~67~1~4~Female~1~1~51~43~0~2~5~1
474~41140~67~1~4~Female~1~1~51~43~0~2~5~1
475~84688~67~1~2~Female~1~1~50~57~1~2~4~1
476~84688~67~1~2~Female~1~1~50~57~1~2~4~1
477~84688~67~1~2~Female~1~1~50~57~1~2~4~1
478~84688~67~1~2~Female~1~1~50~57~1~2~4~1
479~26128~67~1~4~Male~2~1~27~56~1~2~1~0
480~26128~67~1~4~Male~2~1~27~56~1~2~1~0
481~26128~67~1~4~Male~2~1~27~56~1~2~1~0
482~26128~67~1~4~Male~2~1~27~56~1~2~1~0
483~81797~67~1~2~Female~3~1~38~40~1~2~2~0
484~81797~67~1~2~Female~3~1~38~40~1~2~2~0
485~81797~67~1~2~Female~3~1~38~40~1~2~2~0
486~81797~67~1~2~Female~3~1~38~40~1~2~2~0
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488~79197~69~0~3~Female~3~1~40~58~1~1~2~0
489~79197~69~0~3~Female~3~1~40~58~1~1~2~0
490~79197~69~0~3~Female~3~1~40~58~1~1~2~0
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493~27933~69~1~1~Male~2~0~39~91~1~2~2~1
494~27933~69~1~1~Male~2~0~39~91~1~2~2~1
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496~59382~70~0~1~Female~3~1~23~29~1~0~0~0
497~59382~70~0~1~Female~3~1~23~29~1~0~0~0
498~59382~70~0~1~Female~3~1~23~29~1~0~0~0
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500~33548~70~0~1~Female~4~0~31~77~1~0~2~0
501~33548~70~0~1~Female~4~0~31~77~1~0~2~0
502~33548~70~0~1~Female~4~0~31~77~1~0~2~0
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505~30371~71~1~2~Male~2~1~43~35~0~2~4~0
506~30371~71~1~2~Male~2~1~43~35~0~2~4~0
507~77258~71~1~4~Male~3~0~40~95~1~2~2~1
508~77258~71~1~4~Male~3~0~40~95~1~2~2~1
509~77258~71~1~4~Male~3~0~40~95~1~2~2~1
510~77258~71~1~4~Male~3~0~40~95~1~2~2~1
511~15006~71~0~2~Male~1~1~59~11~1~1~5~0
512~15006~71~0~2~Male~1~1~59~11~1~1~5~0
513~15006~71~0~2~Male~1~1~59~11~1~1~5~0
514~15006~71~0~2~Male~1~1~59~11~1~1~5~0
515~67441~71~1~2~Male~3~0~38~75~0~2~2~1
516~67441~71~1~2~Male~3~0~38~75~0~2~2~1
517~67441~71~1~2~Male~3~0~38~75~0~2~2~1
518~67441~71~1~2~Male~3~0~38~75~0~2~2~1
519~24272~71~1~4~Male~2~0~47~9~1~2~4~0
520~24272~71~1~4~Male~2~0~47~9~1~2~4~0
521~24272~71~1~4~Male~2~0~47~9~1~2~4~0
522~24272~71~1~4~Male~2~0~47~9~1~2~4~0
523~23148~71~0~2~Male~4~0~39~75~1~1~2~1
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525~23148~71~0~2~Male~4~0~39~75~1~1~2~1
526~23148~71~0~2~Male~4~0~39~75~1~1~2~1
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529~60538~72~0~0~Female~3~0~25~34~1~0~0~0
530~60538~72~0~0~Female~3~0~25~34~1~0~0~0
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532~58769~72~0~1~Female~4~0~31~71~0~0~2~0
533~58769~72~0~1~Female~4~0~31~71~0~0~2~0
534~58769~72~0~1~Female~4~0~31~71~0~0~2~0
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588~50370~77~0~4~Female~3~1~32~74~0~0~2~0
589~50370~77~0~4~Female~3~1~32~74~0~0~2~0
590~50370~77~0~4~Female~3~1~32~74~0~0~2~0
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703~58725~88~1~2~Male~4~0~58~15~0~2~5~0
704~58725~88~1~2~Male~4~0~58~15~0~2~5~0
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706~58972~88~0~1~Male~2~0~27~69~0~0~1~0
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716~12217~93~0~1~Male~3~1~35~90~1~0~2~1
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730~25000~99~1~2~Female~4~1~41~39~1~2~2~0
731~25000~99~1~2~Female~4~1~41~39~1~2~2~0
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735~70881~103~0~0~Female~1~0~32~69~0~0~2~0
736~70881~103~0~0~Female~1~0~32~69~0~0~2~0
737~70881~103~0~0~Female~1~0~32~69~0~0~2~0
738~12230~19~~2~Female~4~0~30~88~~1~0~1
KNearestNeighbors.py
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 24 14:58:26 2021
@author: David
"""
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScale
from sklearn.neighbors import KNeighborsClassifie
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import pandas as pd
def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
# setup marker generator and color map
markers = ('s', 'x', 'o', '^', 'v')
colors = ('gray', 'indigo', 'purple','yellow', 'gray')
cmap = ListedColormap(colors[:len(np.unique(y))])
# plot the decision surface
x1_min, x1_max = X[:, 0].min XXXXXXXXXX, X[:, 0].max XXXXXXXXXX
x2_min, x2_max = X[:, 1].min XXXXXXXXXX, X[:, 1].max XXXXXXXXXX
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
Z = classifier.predict(np.a
ay([xx1.ravel(), xx2.ravel()]).T)
Z = Z.reshape(xx1.shape)
plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())
# plot all samples
X_test, y_test = X[test_idx, :], y[test_idx]
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
XXXXXXXXXXalpha=0.8, c=cmap(idx),
XXXXXXXXXXmarker=markers[idx], label=cl)
# highlight test samples
if test_idx:
X_test, y_test = X[test_idx, :], y[test_idx]
plt.scatter(X_test[:, 0], X_test[:, 1], c='',
XXXXXXXXXXalpha=1.0, linewidth=1, marker='o',
XXXXXXXXXXs=55, label='test set')
# Importing the dataset
dataset = pd.read_csv(r'C:\Users\David\OneDrive - Savannah State University\VisitJamaica_final.csv', sep="~")
X = dataset.iloc[:, [0, 1]].values
y = dataset.iloc[:, 2].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
sc = StandardScaler()
X_train_std = sc.fit_transform(X_train)
X_test_std = sc.fit_transform(X_test)
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
knn.fit(X_train_std, y_train)
plot_decision_regions(X_combined_std, y_combined,
XXXXXXXXXXclassifier=knn, test_idx=range(600,725))
plt.title('K-NN (Training set)')
plt
project-attached-files-may-5-2021-512-pm-l3k10yhz.zip
Answered 2 days After May 05, 2021

Solution

Uttam answered on May 08 2021
146 Votes
3109 _ Project.docx
CISM3109: Data Analytics
Project: Analysis using SVM, Decision Trees & KNN
Spring 2021
Please submit all relevant:
· Plots & Graphs
· Confusion Matrices
· Lines of Code you modify.
· Complete modified Python program
· Spreadsheet
Data Preparation:
Perform and Submit output for the following:
· Import the text file in into Excel using “,” separators.
· Clean the Data Set, save in .CSV format and submit
· remove the 5 rows with missing data (paste below):
Answer (2.5 points):
dataset.dropna(how='any').reset_index(drop=True)
·
· remove the 5 rows with fields having the wrong data format (paste below:)
· Answer (2.5 points):
· dataset = dataset.drop(dataset.index[695:696]).reset_index(drop=True)
dataset = dataset.drop(dataset.index[398:399]).reset_index(drop=True)
dataset = dataset.drop(dataset.index[265:266]).reset_index(drop=True)
dataset = dataset.drop(dataset.index[216:217]).reset_index(drop=True)
·
· State 3 columns which in your opinion would not be useful for the following analysis
· Answer (3 points):
1. CustomerID
2. Zip Code
3. Spending Score(1-100)
· Save the .CSV file and upload (2 points):
· dataset.to_csv('CleanCSV.csv')
For each question:
Copy these sub questions below Question 1, 2 & 3. Provide responses to each sub question. Submit plots, code & data for all questions:
ANALYSIS
· Using the dataset provided, create a model for the question. You can choose between SVM, Decision Trees and KNN. You may use only one model for each question. Perform the analysis using different combinations of columns to find the column(s) which best explain the Dependent variable. In the case of SVM & KNN, the model should have accuracy > 95% (artificial data).
· Why do you believe the model you chose to answer this question is most appropriate?
· Answer (3 x 3 points):
· Because It gives the good accuracy other than models
· Partition the data so that the training set is 75, 80, 85% reepectively. Paste the head of the of X-train & y_test
· Paste outputs below (2 x 3 points):
· From your analysis, which column(s) provide the best accuracy? State column & percentage.
Answer (10 x 3 points):
Column -- ALL-INCLUSIVE
Percentage is – 52%
· What is the story behind these columns? How do you think they explain the Y-value?
· Answer (3 x 3 points): They explain the y-values because it contains integer values and for the co
elation it contains strongly co
elation with independent columns
· Considering False Negatives & False Positives
(not relevant to the Decision Tree case)
· Which do you want to minimize? Why?
· Answer (2 x 3 points):
· State the value of the parameter (e.g. ‘K’) which minimizes the answer given above.
· Answer (1 x 3 point):
· Bonus: What data column do you think is missing that would improve the accuracy? Why?
· Answer (2 x 3 points):
OUTPUTS
· Modify the Titles to include “CISM3109 Project – Q(1|2|3)” (.5 x 3 point)
· Use SSU colors for the Plots (Blue and Orange) (.5 x 3 point)
· Modify the axes to include the unit of measure (e.g. “Education in Years”) (.5 x 3 point)
· Briefly describe the plot.
· Paste the PLOT Below to get the points listed above:
· Interpret the confusion matrix – explain a False Positive and a False Negative from the matrix.
· Answer (2 x 3 points):
· Paste the CM Below to get the points listed above:
Question-1
We want you make it Jamaica! So we are deciding how to craft our message to you, to convince you to come back again. We find that are groups of people who tend to have visited a certain number of times. Find the best single column to use as a predictor for the number of times visited.
· Never visited
· Been to Jamaica once
· Visited twice
· Vacationed there 4 times
· Made 5 visits
· Did 7 tours
ANSWER- AGE is the best column to use as a predicto
· Why do you believe the model you chose to answer this question is most appropriate?
· Answer (3 x 3 points): I have choosed Knn model as the dataset contains 6 classes in the columns number of visits and to solve a classification problem I found knn as best model.
· Partition the data so that the training set is 75, 80, 85% reepectively. Paste the head of the of X-train & y_test
· Paste outputs below (2 x 3 points):
· When 75%
X_train:
Y_test:
· When 80%
X_train:
Y_test:
· When 85%
X_train:
Y_test:
· From your analysis, which column(s) provide the best accuracy? State column & percentage.
· Answer (10 x 3 points):
· Age and 98%
· What is the story behind these columns? How do you think they explain the Y-value?
· Answer (3 x 3 points):
· This columns share the best co
elation with the Y-value .i.e 0.98
· Considering False Negatives & False Positives
(not relevant to the Decision Tree case)
· Which do you want to minimize? Why?
· Answer (2 x 3 points):
· False negatives should be minimized as in this case if false negatives are more then the important customers that visits often will be missed so it should be minimized.
· State the value of the parameter (e.g. ‘K’) which minimizes the answer given above.
· Answer (1 x 3 point):
· k=3
· Bonus: What data column do you think is missing that would improve the accuracy? Why?
· Answer (2 x 3 points):
OUTPUTS
· Modify the Titles to include “CISM3109 Project – Q(1|2|3)” (.5 x 3 point)
· Use SSU colors for the Plots (Blue and Orange) (.5 x 3 point)
· Modify the axes to include the unit of measure (e.g. “Education in Years”) (.5 x 3 point)
· Briefly describe the plot.
· Paste the PLOT Below to get the points listed above:
· Interpret the confusion matrix – explain a False Positive and a False Negative from the matrix.
· Answer (2 x 3 points): False negatives are the customers who visits the store most often but are predicted as they don’t visit much. Which False positive are the customers that are predicted to visit often but actually they don’t visit much.
· Paste the CM Below to get the points listed above:
[[32 0 0 0 0 0]
[ 0 13 0 0 0 0]
[ 0 0 52 0 0 0]
[ 0 0 0 42 0 0]
[ 0 0 0 3 24 0]
[ 0 0 0 0 3 13]]
Also, If you had to make a quick summary by eyeballing the plot, describe the group of persons who have visited twice & Did 7 tours?
Answer (2 points):
Question-2)
Many visitors like to go out and explore on their own, meet the locals, and eat road food like Anthony Bourdain. CNN said that among 8 reasons, the food alone is itself is reason to visit! We are also happy to take care of your every need and pamper you, so you won’t have to lift a finger. With just a couple phone calls we a
ange your all-inclusive packages Find the best two columns to use as a predictor of whether a visitor will plan and explore on your own (1) or make an all-inclusive a
angement (0).
Additionally, now that you know the predictors locate an example for an explorer (1) and all-inclusive vacationer (0). Give the attributes of each person using the two columns you used as a predictor.
Answer (2 points): The Two best columns that are used as predictors here are Age and NUMBER-VISITS
· Why do you believe the model you chose to answer this question is most appropriate?
· Answer (3 x 3 points): Decision tree regression observes features of an object and trains a model in the structure of a tree to predict data in the future to produce meaningful continuous output. Continuous output means that the output
esult is not discrete, i.e., it is not represented just by a discrete, known set of numbers or values.
· From your analysis, which column(s) provide the best accuracy? State column & percentage.
· Answer (10 x 3 points):
· Age and 57%
· What is the story behind these columns? How do you think they explain the Y-value?
· Answer (3 x 3 points):
· This columns share the best co
elation with the Y-value .i.e 0.57
Question-3)
Jamaica is not one-dimensional in its offerings. You can go jet-skiing, parasailing, cliff-diving, or even get the opportunity to pet alligators. There is even a metallic bob-sled ride! The island also offers invigorating mountain hikes, horse-back riding, snorkeling among beautiful corals. Some visitors just really like the Irie mellow vibe and prefer rafting along the river, leisurely walks along beautiful nature trails, and if you really just want to do nothing at all, there are miles of white sand beaches on which you can sun-bathe. Plus don’t forget, like Savannah, Jamaica is a wedding destination. Find the best two columns to use as a predictor of the touristy ACTIVITIES that a tourist will enjoy while in Jamaica.
To finish your analysis, now that you know the predictors, locate an example for each type of visitor who would be interested in a video of someone petting an alligator-petting, snorkeling, and sun-bathing. Describe each person using the attributes from the two columns you used as a predictor.
Answer (3 points):
Each code matches to an ACTIVITY PACKAGE as follows:
        Code
        Package
        Activities included
        0:
        X-Sports
         Cliff Jumping, alligator petting, Water-Sports
        
        
        
        1:
        Active:
        Mountain Hiking, Horse-back riding, snorkeling along coral
        
        
        
        2:
        Leisure:
        Nature Trails, Sun-bathing, Rafting on the Rio Grande.
CleanCSV.csv
,CustomerID,Zip Code,Annual Income (k$),Spouse,Children,Gender,Miles from Work,Has Winter,Age,Spending Score...
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