Analyzing Big data

Question 1

What are some practical applications of data mining, particularly in the financial sector, and why is avoiding false positives important?

Answer 1

• Data mining can be used in the financial sector to identify new market segments, find product-market matches, and conduct market basket analysis to understand customer behavior.
• Avoiding false positives is crucial because incorrect pattern identification can lead to misguided business decisions and strategies.
• It is essential to carefully verify any identified patterns using separate datasets to ensure their validity and reliability.

Question 2

What are the different types of predictive analytics, and how are they used to predict future outcomes or categorize information?

Answer 2

Predictive analytics involves two main types:
* Numerical Prediction: This type forecasts numerical values, such as future customer purchases or disease remission likelihood. Linear regression is a common method used for this purpose.
* Categorical Prediction: This type categorizes data, such as identifying objects in photos. Several techniques are employed, including:
* K-means clustering: Groups data into a predetermined number of clusters.
K-nearest neighbors: Classifies data based on similar data points.
Decision trees and random forests: Create decision rules based on data features.
Neural networks (including deep learning): Model complex patterns in data.

These methods mimic human judgment by predicting future outcomes or categorizing information, often used in fraud detection by identifying outliers and matching patterns of fraudulent activity using algorithms like regression, classification trees, decision trees, and neural networks.