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Credit Card Fraud Detection

Predict fraudulent credit card transactions with the help of Machine learning models.

Import the following libraries to get started.

import numpy as np
import pandas as pd
import time


import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

from sklearn import metrics
from sklearn import preprocessing
from sklearn.preprocessing import PowerTransformer

import warnings
warnings.filterwarnings('ignore')

Exploratory data analysis

df = pd.read_csv('../input/creditcardfraud/creditcard.csv')
df.head()
#observe the different feature type present in the data
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 284807 entries, 0 to 284806 Data columns (total 31 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Time 284807 non-null float64 1 V1 284807 non-null float64 2 V2 284807 non-null float64 3 V3 284807 non-null float64 4 V4 284807 non-null float64 5 V5 284807 non-null float64 6 V6 284807 non-null float64 7 V7 284807 non-null float64 8 V8 284807 non-null float64 9 V9 284807 non-null float64 10 V10 284807 non-null float64 11 V11 284807 non-null float64 12 V12 284807 non-null float64 13 V13 284807 non-null float64 14 V14 284807 non-null float64 15 V15 284807 non-null float64 16 V16 284807 non-null float64 17 V17 284807 non-null float64 18 V18 284807 non-null float64 19 V19 284807 non-null float64 20 V20 284807 non-null float64 21 V21 284807 non-null float64 22 V22 284807 non-null float64 23 V23 284807 non-null float64 24 V24 284807 non-null float64 25 V25 284807 non-null float64 26 V26 284807 non-null float64 27 V27 284807 non-null float64 28 V28 284807 non-null float64 29 Amount 284807 non-null float64 30 Class 284807 non-null int64 dtypes: float64(30), int64(1) memory usage: 67.4 MB