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- create table credit_card_frauds(
- credit_card_no bigint,
- cardholder_name varchar(255),
- cardholder_home_address varchar(255),
- billing_address varchar(255),
- transaction_amt int,
- transaction_mode varchar(255),
- transaction_date_time datetime,
- country varchar(255),
- type_of_card varchar(255)
- );
- create table cyber_crime(
- crime_name varchar(255),
- crime_date_time datetime,
- crime_city varchar(255),
- crime_country varchar(255),
- victim_name varchar(255)
- );
- insert into credit_card_frauds values
- (
- 7889123451439641,
- "Dhruv Sachdeva",
- "Karnal,Haryana",
- "Preet Vihar",
- 6500,
- "POS",
- "2022-10-12 15:25:30",
- "India",
- "Diner Club"
- ),
- (
- 4522620045711987,
- "Harmandeep Singh Mavi",
- "Sangroor, Punjab",
- "Navi Mumbai",
- 25000,
- "online",
- "2022-12-02 16:30:34",
- "Canada",
- "American Express"
- ),
- (
- 2400157736524271,
- "Tanmay Singh",
- "Daman",
- "Gurugram",
- 2412,
- "POS",
- "2021-07-28 12:05:11",
- "India",
- "Visa"
- ),
- (
- 6522781254238514,
- "Kumar Jyotirmay",
- "Dhaka, Bihar",
- "Delhi",
- 1650,
- "online",
- "2022-02-12 04:30:10",
- "India",
- "Mastercard"
- ),
- (
- 4590254412758963,
- "Shailesh Dhaundiyal",
- "Pawri,Uttarakhand",
- "Uttam Nagar, Delhi",
- 43000,
- "online",
- "2022-03-02 04:14:20",
- "India",
- "Rupay"
- );
- insert into cyber_crime values
- (
- "Phishing",
- "2022-01-02 07:14:20",
- "Saharanpur",
- "India",
- "Harsh Aggarwal"
- ),
- (
- "Identity Theft",
- "2022-01-03 15:13:25",
- "Indore",
- "India",
- "Fulchand Sahoo"
- ),
- (
- "Data Breach",
- "2022-02-28 13:10:20",
- "Delhi",
- "India",
- "Deva Kaushik"
- ),
- (
- "Harrasment",
- "2021-01-10 10:25:20",
- "Karnal",
- "India",
- "Dhruv Sachdeva"
- ),
- (
- "Cyber Extortion",
- "2022-01-28 05:10:20",
- "Pawri",
- "India",
- "Shailesh Daundiyal"
- );
- select * from credit_card_frauds inner join cyber_crime on credit_card_frauds.cardholder_name = cyber_crime.culprit_name;
- select * from credit_card_frauds where transaction_amt >= 25000;
- select distinct type_of_card, count(type_of_card) from credit_card_frauds group by type_of_card;
- select crime_country, count(crime_country) from cyber_crime group by crime_country;
- select distinct crime_name from cyber_crime;
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