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"""Credit Card Transactions |
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Automatically generated by Colab. |
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Original file is located at |
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https://colab.research.google.com/drive/1u6Uvg6spSXdnjrvtQi8OjhJOGywYvsNG |
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""" |
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import numpy as np |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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from sklearn.tree import DecisionTreeClassifier |
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from sklearn.model_selection import train_test_split |
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from sklearn.ensemble import RandomForestClassifier |
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from sklearn.model_selection import GridSearchCV |
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from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, ConfusionMatrixDisplay |
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from sklearn.ensemble import GradientBoostingClassifier |
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df = pd.read_csv('creditcard.csv') |
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df.head() |
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df.shape |
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df.columns |
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df.info() |
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df.describe() |
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df.isnull().sum() |
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df.duplicated().sum() |
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df.drop_duplicates(inplace=True) |
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df.shape |
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df['Class'].unique() |
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df['Class'].value_counts() |
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fraud = df[df['Class'] == 1] |
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normal = df[df['Class'] == 0] |
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normal_percentage = len(normal)/(len(fraud)+len(normal)) |
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fraud_percentage = len(fraud)/(len(fraud)+len(normal)) |
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print('Percentage of fraud transactions = ', round(fraud_percentage * 100, 3)) |
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print('Percentage of normal transactions = ', round(normal_percentage * 100, 3)) |
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plt.figure(figsize=(9,7)) |
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sns.countplot(data=df,x='Class',palette=['blue', 'red']) |
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plt.title("Number of Normal and Fraud Transactions"); |
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plt.figure(figsize=(8,6)) |
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sns.FacetGrid(df, hue="Class", height=6,palette=['blue','red']).map(plt.scatter, "Time", "Amount").add_legend() |
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plt.show() |
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plt.figure(figsize=(10,7)) |
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sns.heatmap(data=df.corr(),cmap='mako') |
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plt.show() |
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X = df.drop('Class',axis=1) |
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y = df['Class'] |
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) |
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def model_train_test(model,X_train,y_train,X_test,y_test): |
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model.fit(X_train,y_train) |
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prediction = model.predict(X_test) |
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print('Accuracy = {}'.format(accuracy_score(y_test,prediction))) |
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print(classification_report(y_test,prediction)) |
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matrix = confusion_matrix(y_test,prediction) |
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dis = ConfusionMatrixDisplay(matrix) |
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dis.plot() |
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plt.show() |
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rf_model = RandomForestClassifier() |
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model_train_test(rf_model,X_train,y_train,X_test,y_test) |
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Decision_tree = DecisionTreeClassifier() |
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model_train_test(Decision_tree,X_train,y_train,X_test,y_test) |
