Credit Card Fraud
This dataset consists of credit card transactions in the western United States. It includes information about each transaction including customer details, the merchant and category of purchase, and whether or not the transaction was a fraud.
Not sure where to begin? Scroll to the bottom to find challenges!
import pandas as pd
ccf = pd.read_csv('credit_card_fraud.csv')
ccf.head(5)Data Dictionary
| transdatetrans_time | Transaction DateTime |
|---|---|
| merchant | Merchant Name |
| category | Category of Merchant |
| amt | Amount of Transaction |
| city | City of Credit Card Holder |
| state | State of Credit Card Holder |
| lat | Latitude Location of Purchase |
| long | Longitude Location of Purchase |
| city_pop | Credit Card Holder's City Population |
| job | Job of Credit Card Holder |
| dob | Date of Birth of Credit Card Holder |
| trans_num | Transaction Number |
| merch_lat | Latitude Location of Merchant |
| merch_long | Longitude Location of Merchant |
| is_fraud | Whether Transaction is Fraud (1) or Not (0) |
Source of dataset. The data was partially cleaned and adapted by DataCamp.
Scenarios are broader questions to help you develop an end-to-end project for your portfolio:
A new credit card company has just entered the market in the western United States. The company is promoting itself as one of the safest credit cards to use. They have hired you as their data scientist in charge of identifying instances of fraud. The executive who hired you has have provided you with data on credit card transactions, including whether or not each transaction was fraudulent.
The executive wants to know how accurately you can predict fraud using this data. She has stressed that the model should err on the side of caution: it is not a big problem to flag transactions as fraudulent when they aren't just to be safe. In your report, you will need to describe how well your model functions and how it adheres to these criteria.
You will need to prepare a report that is accessible to a broad audience. It will need to outline your motivation, analysis steps, findings, and conclusions.
Check the Data Set
import the packages required
# For data wrangling
import numpy as np
import pandas as pd
# For visualization
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
pd.options.display.max_rows = None
pd.options.display.max_columns = None
import plotly.graph_objects as gocheck what sort of data and any null values by surface
ccf.info()There are no null values present in the dataset. There are 9 numerical values and 8 categorical value fields in the data set
# Basic statistics
# Set display format to avoid scientific notation
pd.options.display.float_format = '{:.2f}'.format
# summary statistics
summary_statistics = ccf.describe()
summary_statistics.T
#print(df.describe())By using we can identify the ranges where our data disperse
Desecting date time column in to different fields
# Convert 'trans_date_trans_time' to datetime format
ccf['trans_date_trans_time'] = pd.to_datetime(ccf['trans_date_trans_time'])
# Create separate columns for date and time
ccf['trans_date'] = ccf['trans_date_trans_time'].dt.date
ccf['trans_time'] = ccf['trans_date_trans_time'].dt.time
# Extract year, month, day, hour, minute, and second into separate columns
ccf['year'] = ccf['trans_date_trans_time'].dt.year
ccf['month'] = ccf['trans_date_trans_time'].dt.month
ccf['day'] = ccf['trans_date_trans_time'].dt.day
ccf['hour'] = ccf['trans_date_trans_time'].dt.hour
ccf['minute'] = ccf['trans_date_trans_time'].dt.minute
ccf['second'] = ccf['trans_date_trans_time'].dt.second
# Convert 'trans_date' to string format YYYY-MM-DD
ccf['trans_date'] = ccf['trans_date'].astype(str)