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
pd.read_csv('credit_card_fraud.csv')
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.
Don't know where to start?
Challenges are brief tasks designed to help you practice specific skills:
- 🗺️ Explore: What types of purchases are most likely to be instances of fraud? Consider both product category and the amount of the transaction.
- 📊 Visualize: Use a geospatial plot to visualize the fraud rates across different states.
- 🔎 Analyze: Are older customers significantly more likely to be victims of credit card fraud?
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.
✍️ If you have an idea for an interesting Scenario or Challenge, or have feedback on our existing ones, let us know! You can submit feedback by pressing the question mark in the top right corner of the screen and selecting "Give Feedback". Include the phrase "Content Feedback" to help us flag it in our system.
cc_fraud=pd.read_csv('credit_card_fraud.csv')
cc_fraud.head()cc_fraud_is=cc_fraud[cc_fraud['is_fraud']==1]
more_1=cc_fraud_is.groupby('dob').merchant.value_counts()
more_1=more_1[more_1>1]
df=pd.DataFrame(more_1,)
double_fraud=df.rename(columns={'merchant':'fraud_cnt'})
double_fraud=double_fraud.reset_index()
double_fraud=double_fraud[['merchant','dob','fraud_cnt']]
double_fraudcc_fraud_is.loc[(cc_fraud_is['dob']=='1928-10-01') & (cc_fraud_is['merchant']=='Welch Inc')]
lynch_moh=cc_fraud_is[cc_fraud_is['merchant'].str.contains('Romaguera, Cruickshank and Greenholt')]
lynch_mohimport matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline#Calculating fraud count by purchase category
cc_fraud_by_cat=cc_fraud.groupby('category').is_fraud.sum()
cc_fraud_by_cat#Plotting fraud count distribution by purchase catogory
plt.bar(x=cc_fraud_by_cat.index,height=cc_fraud_by_cat)
plt.xticks(rotation=90)
plt.ylabel('Fraud count')
plt.show()
is_fraud=cc_fraud[cc_fraud['is_fraud']==1]
is_fraud_sorted=is_fraud.groupby('category').amt.sum().sort_values()
is_fraud_sorted#Calculating fraud count per state
cc_fraud_by_st=cc_fraud.groupby('state').is_fraud.sum()
cc_fraud_by_st