Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extract features from images.
Importing the Relevant Libraries¶
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
Importing the Dataset¶
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url = "https://datascienceschools.github.io/Machine_Learning/Classification_Models_CaseStudies/BankNote_Authentication.csv"
df = pd.read_csv(url)
df.head()
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Declaring the Dependent & the Independent Variables¶
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X = df.iloc[:,:-1].values
y = df.iloc[:, -1].values
Splitting the Dataset into the Training Set and Test Set¶
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from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)
Training the Random Forest Model¶
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from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)
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Feature Importances¶
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df_feature = df.drop('class', axis=1)
feature_importances = pd.DataFrame(data = df_feature.columns.values, columns = ['Features'])
feature_importances['Importance'] = model.feature_importances_
feature_importances.sort_values('Importance',ascending=False)
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Predicting the Test Set Results¶
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y_pred = model.predict(X_test)
Confusion Matrix¶
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from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy is: {:.2f} %".format(accuracy*100))
sns.heatmap(cm, annot=True, fmt='d')
plt.show()
Classification Report¶
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from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))
K-Fold Cross Validation¶
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from sklearn.model_selection import cross_val_score
accuracies = cross_val_score(estimator = model, X = X_train, y = y_train, cv = 9)
print("Accuracy: {:.2f} %".format(accuracies.mean()*100))
print("Standard Deviation: {:.2f} %".format(accuracies.std()*100))
Saving the Model¶
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import pickle
pickle_out = open("RFClassifier.pkl","wb")
pickle.dump(model, pickle_out)
pickle_out.close()
Making Prediction (New Dataset)¶
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url = "https://datascienceschools.github.io/Machine_Learning/Classification_Models_CaseStudies/BankNote_Authentication_Test.csv"
new_data = pd.read_csv(url)
new_data.head()
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Declaring Independent Variables¶
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X_test = new_data.iloc[:,:].values
Predicting Dependent Variable (class)¶
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y_pred_test = model.predict(X_test)
new_data['predicted_Survive'] = y_pred_test
new_data
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