Hi, you have a great set of questions! Let’s break it down step by step for your dataset involving bank customers.
1. Data Mining Technique:
—>Recommended Technique: Classification
If your goal is to predict a categorical outcome, for example, whether a customer will default on a loan, or what type of loan they are likely to take—then classification is preferred.
You have categorical variables like reason for loan, credit rating, and reviews (good or bad ), which make classification ideal.
- Classification algorithms like decision trees, random forest, or logistic regression can help in identifying patterns and predicting outcomes.
Other options briefly:
- Regression: Used for predicting continuous values like income. Less suitable if your main outcome is categorical.
- Clustering: Good for grouping similar customers without pre-defined labels (e.g., customer segmentation).
- Nearest Neighbor: Useful for finding similar customers based on profile, but not as scalable or interpretable as classification.
2. One-way ANOVA:
—>Two example variables:
- Independent variable (categorical): Educational Level (e.g., High School, Bachelor's, Master's, etc.)
- Dependent variable (numerical): Income Level
—>What One-Way ANOVA tests:
It tests whether there is a statistically significant difference in the means of a numerical variable across multiple groups**.
- In this case, it would test whether the average income level significantly differs based on education level.
3. Cross-tabulation and Chi-Square Test:
Two example variables:
- Credit Rating(e.g., Good, Average, Poor)
- Review of Previous Bank Experience (e.g., Positive, Neutral, Negative)
—>What the Chi-Square Test would test:
- It would assess whether there is a significant association between two categorical variables.
- In this case, it would test whether credit rating is related to customers’ reviews of their previous bank experience
