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 libraries 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 StandardScaler 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), np.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.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape), alpha = 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), np.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.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape), alpha = 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 libraries 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 DecisionTreeRegressor regressor = DecisionTreeRegressor(random_state = 0) regressor.fit(X, y) # Predicting a new result regressor.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 CustomerID~Zip Code~Annual Income 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698~56544~88~0~3~Female~1~1~52~13~1~1~5~0 699~56544~88~0~3~Female~1~1~52~13~1~1~5~0 700~56544~eighty eight~0~3~Female~1~1~52~5~13~1~0~1 701~58725~88~1~2~Male~4~0~58~15~0~2~5~0 702~58725~88~1~2~Male~4~0~58~15~0~2~5~0 703~58725~88~1~2~Male~4~0~58~15~0~2~5~0 704~58725~88~1~2~Male~4~0~58~15~0~2~5~0 705~58972~88~0~1~Male~2~0~27~69~0~0~1~0 706~58972~88~0~1~Male~2~0~27~69~0~0~1~0 707~58972~88~0~1~Male~2~0~27~69~0~0~1~0 708~58972~88~0~1~Male~2~0~27~69~0~0~1~0 709~17704~93~0~2~Male~1~0~59~14~0~1~5~0 710~17704~93~0~2~Male~1~0~59~14~0~1~5~0 711~17704~93~0~2~Male~1~0~59~14~0~1~5~0 712~17704~93~0~2~Male~1~0~59~14~0~1~5~0 713~12217~93~0~1~Male~3~1~35~90~1~0~2~1 714~12217~93~0~1~Male~3~1~35~90~1~0~2~1 715~12217~93~0~1~Male~3~1~35~90~1~0~2~1 716~12217~93~0~1~Male~3~1~35~90~1~0~2~1 717~12777~97~0~0~Female~1~1~37~32~0~1~2~0 718~12777~97~0~0~Female~1~1~37~32~0~1~2~0 719~12777~97~0~0~Female~1~1~37~32~0~1~2~0 720~12777~97~0~0~Female~1~1~37~32~0~1~2~0 721~62745~97~1~1~Female~4~1~32~86~1~2~2~0 722~62745~97~1~1~Female~4~1~32~86~1~2~2~0 723~62745~97~1~1~Female~4~1~32~86~1~2~2~0 724~62745~97~1~1~Female~4~1~32~86~1~2~2~0 725~27093~98~1~0~Female~1~1~29~88~1~~1~0 726~27093~98~1~0~Female~1~1~29~88~1~2~1~0 727~27093~98~1~0~Female~1~1~29~88~1~2~1~0 728~27093~98~1~0~Female~1~1~29~88~1~2~1~0 729~25000~99~1~2~Female~4~1~41~39~1~2~2~0 730~25000~99~1~2~Female~4~1~41~39~1~2~2~0 731~25000~99~1~2~Female~4~1~41~39~1~2~2~0 732~25000~99~1~2~Female~4~1~41~39~1~2~2~0 733~39604~99~0~3~Male~3~0~30~1~97~0~1~0 734~70881~103~0~0~Female~1~0~32~69~0~0~2~0 735~70881~103~0~0~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 StandardScaler from sklearn.neighbors import KNeighborsClassifier 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() - .25, X[:, 0].max() + .25 x2_min, x2_max = X[:, 1].min() - .25, X[:, 1].max() + .25 xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution)) Z = classifier.predict(np.array([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], alpha=0.8, c=cmap(idx), marker=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='', alpha=1.0, linewidth=1, marker='o', s=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, classifier=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 AfterMay 05, 2021

Answer To: support_vector_machine.py # Support Vector Machine (SVM) # Importing the libraries import numpy as...

Uttam answered on May 08 2021
133 Votes
3109 _ Project.docx
CISM3109: Data Analytics
Project: Analysis using SVM, Decision Trees & KNN
Spring 2021
Please submit all relevant:
ยท Plots & Graphs
ยท Confusion Matrices
ยท Lines of Code you modify.
ยท Complete modified Python program
ยท Spreadsheet
Data Preparation:
Perform and Submit output for the following:
ยท Import the text file in into Excel using โ€œ,โ€ separators.
ยท Clean the Data Set, save in .CSV format and submit
ยท remove the 5 rows with missing data (paste below):
Answer (2.5 points):
dataset.dropna(how='any').reset_index(drop=True)
ยท
ยท remove the 5 rows with fields having the wrong data format (paste below:)
ยท Answer (2.5 points):
ยท 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)
ยท
ยท State 3 columns which in your opinion would not be useful for the following analysis
ยท Answer (3 points):
1. CustomerID
2. Zip Code
3. Spending Score(1-100)
ยท Save the .CSV file and upload (2 points):
ยท 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
ยท 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).
ยท Why do you believe the model you chose to answer this question is most appropriate?
ยท Answer (3 x 3 points):
ยท Because It gives the good accuracy other than models
ยท Partition the data so that the training set is 75, 80, 85% reepectively. Paste the head of the of X-train & y_test
ยท Paste outputs below (2 x 3 points):
ยท From your analysis, which column(s) provide the best accuracy? State column & percentage.
Answer (10 x 3 points):
Column -- ALL-INCLUSIVE
Percentage is โ€“ 52%
ยท What is the story behind these columns? How do you think they explain the Y-value?
ยท Answer (3 x 3 points): They explain the y-values because it contains integer values and for the correlation it contains strongly correlation with independent columns
ยท Considering False Negatives & False Positives
(not relevant to the Decision Tree case)
ยท Which do you want to minimize? Why?
ยท Answer (2 x 3 points):
ยท State the value of the parameter (e.g. โ€˜Kโ€™) which minimizes the answer given above.
ยท Answer (1 x 3 point):
ยท Bonus: What data column do you think is missing that would improve the accuracy? Why?
ยท Answer (2 x 3 points):
OUTPUTS
ยท Modify the Titles to include โ€œCISM3109 Project โ€“ Q(1|2|3)โ€ (.5 x 3 point)
ยท Use SSU colors for the Plots (Blue and Orange) (.5 x 3 point)
ยท Modify the axes to include the unit of measure (e.g. โ€œEducation in Yearsโ€) (.5 x 3 point)
ยท Briefly describe the plot.
ยท Paste the PLOT Below to get the points listed above:
ยท Interpret the confusion matrix โ€“ explain a False Positive and a False Negative from the matrix.
ยท Answer (2 x 3 points):
ยท 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.
ยท Never visited
ยท Been to Jamaica once
ยท Visited twice
ยท Vacationed there 4 times
ยท Made 5 visits
ยท Did 7 tours
ANSWER- AGE is the best column to use as a predictor
ยท Why do you believe the model you chose to answer this question is most appropriate?
ยท 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.
ยท Partition the data so that the training set is 75, 80, 85% reepectively. Paste the head of the of X-train & y_test
ยท Paste outputs below (2 x 3 points):
ยท When 75%
X_train:
Y_test:
ยท When 80%
X_train:
Y_test:
ยท When 85%
X_train:
Y_test:
ยท From your analysis, which column(s) provide the best accuracy? State column & percentage.
ยท Answer (10 x 3 points):
ยท Age and 98%
ยท What is the story behind these columns? How do you think they explain the Y-value?
ยท Answer (3 x 3 points):
ยท This columns share the best correlation with the Y-value .i.e 0.98
ยท Considering False Negatives & False Positives
(not relevant to the Decision Tree case)
ยท Which do you want to minimize? Why?
ยท Answer (2 x 3 points):
ยท 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.
ยท State the value of the parameter (e.g. โ€˜Kโ€™) which minimizes the answer given above.
ยท Answer (1 x 3 point):
ยท k=3
ยท Bonus: What data column do you think is missing that would improve the accuracy? Why?
ยท Answer (2 x 3 points):
OUTPUTS
ยท Modify the Titles to include โ€œCISM3109 Project โ€“ Q(1|2|3)โ€ (.5 x 3 point)
ยท Use SSU colors for the Plots (Blue and Orange) (.5 x 3 point)
ยท Modify the axes to include the unit of measure (e.g. โ€œEducation in Yearsโ€) (.5 x 3 point)
ยท Briefly describe the plot.
ยท Paste the PLOT Below to get the points listed above:
ยท Interpret the confusion matrix โ€“ explain a False Positive and a False Negative from the matrix.
ยท Answer (2 x 3 points): False negatives are the customers who visits the store most often but are predicted as they donโ€™t visit much. Which False positive are the customers that are predicted to visit often but actually they donโ€™t visit much.
ยท 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 wonโ€™t have to lift a finger. With just a couple phone calls we arrange 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 arrangement (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
ยท Why do you believe the model you chose to answer this question is most appropriate?
ยท 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/result is not discrete, i.e., it is not represented just by a discrete, known set of numbers or values.
ยท From your analysis, which column(s) provide the best accuracy? State column & percentage.
ยท Answer (10 x 3 points):
ยท Age and 57%
ยท What is the story behind these columns? How do you think they explain the Y-value?
ยท Answer (3 x 3 points):
ยท This columns share the best correlation 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 donโ€™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...
SOLUTION.PDF
SOLUTION.PDF

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