File size: 2,280 Bytes
8a10ee0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
# -*- coding: utf-8 -*-
"""Credit Card Transactions
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1u6Uvg6spSXdnjrvtQi8OjhJOGywYvsNG
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, ConfusionMatrixDisplay
from sklearn.ensemble import GradientBoostingClassifier
df = pd.read_csv('creditcard.csv')
df.head()
df.shape
df.columns
df.info()
df.describe()
df.isnull().sum()
df.duplicated().sum()
df.drop_duplicates(inplace=True)
df.shape
df['Class'].unique()
df['Class'].value_counts()
fraud = df[df['Class'] == 1]
normal = df[df['Class'] == 0]
normal_percentage = len(normal)/(len(fraud)+len(normal))
fraud_percentage = len(fraud)/(len(fraud)+len(normal))
print('Percentage of fraud transactions = ', round(fraud_percentage * 100, 3))
print('Percentage of normal transactions = ', round(normal_percentage * 100, 3))
plt.figure(figsize=(9,7))
sns.countplot(data=df,x='Class',palette=['blue', 'red'])
plt.title("Number of Normal and Fraud Transactions");
plt.figure(figsize=(8,6))
sns.FacetGrid(df, hue="Class", height=6,palette=['blue','red']).map(plt.scatter, "Time", "Amount").add_legend()
plt.show()
plt.figure(figsize=(10,7))
sns.heatmap(data=df.corr(),cmap='mako')
plt.show()
X = df.drop('Class',axis=1)
y = df['Class']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
def model_train_test(model,X_train,y_train,X_test,y_test):
model.fit(X_train,y_train)
prediction = model.predict(X_test)
print('Accuracy = {}'.format(accuracy_score(y_test,prediction)))
print(classification_report(y_test,prediction))
matrix = confusion_matrix(y_test,prediction)
dis = ConfusionMatrixDisplay(matrix)
dis.plot()
plt.show()
rf_model = RandomForestClassifier()
model_train_test(rf_model,X_train,y_train,X_test,y_test)
Decision_tree = DecisionTreeClassifier()
model_train_test(Decision_tree,X_train,y_train,X_test,y_test) |