Classification of buildings as per the damage data using SVM - python-3.x

Question

I have a university task to perform. It is regarding the classification of several buildings (with 6 parameters) based on the damage classification (1-5). I did the coding as per the guidance of the SVM, but not sure of the output accuracy. Can you please advise, how can I improve my result and what is the other choices of the algorithm.
'''
# Support Vector Machine (SVM)
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Ehsan Duzce.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 7].values
# Taking care of missing data
from sklearn.impute import SimpleImputer
# creating object for SimpleImputer class as "imputer"
imputer = SimpleImputer(missing_values = np.nan, strategy = "mean", verbose=0)
imputer = imputer.fit(X[:, 1:7]) #upper bound is not included, but lower bound
X[:, 1:7] = imputer.transform(X[:, 1:7])
# Avoiding the dummy Variable Trap
X = X[:, 1:] #To remove the first column from the dataset
# 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)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Fitting SVM to the Training set
from sklearn.svm import SVC
classifier = SVC(kernel = 'poly', degree = 3)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() +
1, step = 0.01), np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1,
step = 0.01))
Xpred = np.array([X1.ravel(), X2.ravel()] + [np.repeat(0, X1.ravel().size) for _ in
range(4)]).T
# Xpred now has a grid for x1 and x2 and average value (0) for x3 through x6
pred = classifier.predict(Xpred).reshape(X1.shape) # is a matrix of 0's and 1's !
plt.contourf(X1, X2, pred, alpha = 1.0, cmap = ListedColormap(('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'))(I))
plt.title('SVM (Training set)')
plt.xlabel('Damage Scale')
plt.ylabel('Building Database')
plt.legend()
plt.show()
# Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() +
1, step = 0.01), np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1,
step = 0.01))
Xpred = np.array([X1.ravel(), X2.ravel()] + [np.repeat(0, X1.ravel().size) for _ in
range(4)]).T
# Xpred now has a grid for x1 and x2 and average value (0) for x3 through x6
pred = classifier.predict(Xpred).reshape(X1.shape) # is a matrix of 0's and 1's !
plt.contourf(X1, X2, pred, alpha = 1.0, cmap = ListedColormap(('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'))(I))
plt.title('SVM (Test set)')
plt.xlabel('Damage Scale')
plt.ylabel('Building Database')
plt.legend()
plt.show()
'''

Answer 1

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First and foremost you should get acquainted with your training data. From what I've understood you simply feed the data to the model without any kind of pre processing on the data, you shouldn't do that.
I see you are inputing missing data with the mean, maybe try and remove the data points and see the results, remove outliers that may "confuse" your model.
Also your plots are not very friendly you tell us the data is classified 1-5, but in the plots [-2,2].
But since your questions is algorithmic specific try hyper-parameter tuning.
You can do it like this:
from sklearn.model_selection import GridSearchCV
param_grid = {'C': [0.1,1, 10, 100], 'gamma': [1,0.1,0.01,0.001],'kernel': ['rbf', 'poly', 'sigmoid']}
grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=2)
grid.fit(X_train,y_train)
print(grid.best_estimator_)
I recommend reading this article, to understand SVM and tune your parameters]
https://towardsdatascience.com/svm-hyper-parameter-tuning-using-gridsearchcv-49c0bc55ce29