Question

Give 4 examples of case problems in different sectors (banking, e-commerce, retail, etc.) that can be solved by classification machine learning. Then write down the data that can be used in the machine learning (y = house price, x1 = area of the house, etc.). The explanation of each case includes at least 100 words, while the data used includes at least 10 variables.

Answer 1

Classification machine learning can solve case problems in various sectors like banking for credit risk assessment, e-commerce for fraud detection, retail for customer segmentation, and healthcare for disease diagnosis, using data variables relevant to each industry.

Step-by-step explanation:

Classification machine learning algorithms can address various case problems across different industries. Here, I'll provide four examples with a brief explanation for each and the data sets that could be used.

Banking: Credit Risk Assessment

Financial institutions use classification models to determine the likelihood of a loan applicant defaulting on a loan. The data used could include:

• Age (x1)
• Employment status (x2)
• Credit score (x3)
• Income level (x4)
• Loan amount (x5)
• Previous default history (x6)
• Marital status (x7)
• Debt-to-income ratio (x8)
• Home ownership status (x9)
• Length of credit history (x10)

E-commerce: Fraud Detection

Online retailers need to quickly identify potentially fraudulent transactions to prevent losses. Data used in these models can include:

• Transaction amount (x1)
• Time of transaction (x2)
• User location (x3)
• Device ID (x4)
• Previous buying habits (x5)
• IP address (x6)
• Payment method used (x7)
• Shipping address discrepancy (x8)
• Account age (x9)
• Number of items purchased (x10)

Retail: Customer Segmentation

Retailers can classify customers into different segments for targeted marketing. Relevant data might be:

• Age (x1)
• Gender (x2)
• Purchase history (x3)
• Income level (x4)
• Geographical location (x5)
• Frequent store visits (x6)
• Loyalty program participation (x7)
• Online vs. in-store purchases (x8)
• Shopping cart abandonment rate (x9)
• Response to past marketing campaigns (x10)

Healthcare: Disease Diagnosis

Healthcare providers use classification to support diagnoses by predicting the presence or absence of a disease. Data used might include:

• Age (x1)
• Gender (x2)
• Genetic factors (x3)
• Body Mass Index (BMI) (x4)
• Blood pressure (x5)
• Cholesterol levels (x6)
• Family medical history (x7)
• Smoking status (x8)
• Exercise frequency (x9)
• Previous medical conditions (x10)