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June 6, 2021 02:27
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MCS2203 - Assignment 1
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| import numpy as np | |
| import pandas as pd | |
| from sklearn.metrics import confusion_matrix | |
| from sklearn.metrics import accuracy_score, precision_score | |
| from sklearn.neighbors import KNeighborsClassifier | |
| from sklearn.svm import SVC | |
| # Calculate sensitivity using the confusion matrix | |
| def calculate_sensitivity(c): | |
| return c[0, 0] / (c[0, 0] + c[0, 1]) | |
| # Calculate specificity using the confusion matrix | |
| def calculate_specificity(c): | |
| return c[1, 1] / (c[1, 0] + c[1, 1]) | |
| # print output (scores) | |
| def print_result(y_prediction, y_test): | |
| print("Accuracy: {0}".format(accuracy_score(y_prediction, y_test))) | |
| print("Precision: {0}\n".format(precision_score(y_test, y_prediction, average="macro"))) | |
| print("Confusion Matrix:") | |
| cm = confusion_matrix(y_prediction, y_test) | |
| sensitivity = calculate_sensitivity(cm) | |
| specificity = calculate_specificity(cm) | |
| print(cm) | |
| print("\nSensitivity: {0}".format(sensitivity)) | |
| print("Specificity: {0}".format(specificity)) | |
| # read datasets. use the openpyxl engine to read from the spreadsheet | |
| training_data = pd.read_excel("./dataset.xlsx", engine="openpyxl", sheet_name="Training-Data") | |
| test_data = pd.read_excel("./dataset.xlsx", engine="openpyxl", sheet_name="Test-Data") | |
| # preprocess training data | |
| # remove the ID column | |
| training_data.drop("ID", axis="columns", inplace=True) | |
| # remove the last three row of the data set showing stats and NaN | |
| training_data.drop(training_data.tail(3).index, inplace=True) | |
| # replace '?' with None | |
| training_data.replace('?', np.nan, inplace=True) | |
| # set average values to None | |
| training_data = training_data.fillna(value=training_data.mean()) | |
| # preprocess test data | |
| # drop ID column | |
| test_data.drop("ID", axis="columns", inplace=True) | |
| # drop last 4 rows | |
| test_data.drop(test_data.tail(4).index, inplace=True) | |
| # replace '?' with None and set mean to None | |
| test_data.replace('?', np.nan, inplace=True) | |
| # test_data.apply(lambda x: x.fillna(x.mean()), axis=0) | |
| test_data = test_data.fillna(value=test_data.mean()) | |
| # split x (features) and y from the training dataset | |
| x_training = training_data.loc[:, 'A1':'A9'] | |
| y_training = training_data[["Class"]].values.flatten() | |
| # split x (features) and y from the test dataset | |
| x_test = test_data.loc[:, 'A1':'A9'] | |
| y_test = test_data[["Class"]].values.flatten() | |
| # SVC Model | |
| print("Training SVC Model") | |
| SVC_model = SVC() | |
| SVC_model.fit(x_training, y_training) | |
| SVC_prediction = SVC_model.predict(x_test) | |
| print_result(SVC_prediction, y_test) | |
| # KNN model | |
| print("\n\nTraining KNN Model") | |
| KNN_model = KNeighborsClassifier(n_neighbors=5) | |
| KNN_model.fit(x_training, y_training) | |
| KNN_prediction = KNN_model.predict(x_test) | |
| print_result(KNN_prediction, y_test) |
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