Question

1. Discuss the basic statistical models used in descriptive data mining.
2. Discuss the various predictive models for data mining. Provide a comprehensive answer.
3. Identify which models are applicable for which types of predictive scenarios.

Answer 1

predictive models for data mining:

Predictive data mining, which produces the model of the system described by the given data set.

some of the different types of predictive models are:

• Ordinary Least Squares
• Generalized Linear Models (GLM)
• Logistic Regression
• Random Forests
• Decision Trees
• Neural Networks
• Multivariate Adaptive Regression Splines (MARS)

Each of these types has a particular use and answers a specific question or uses a certain type of dataset. Despite the methodological and mathematical differences among the model types, the overall goal of each is similar: to predict future or unknown outcomes based on data about past outcomes.

Smart Predict is currently able to train predictive models that deal with classification, regression, and time-series forecasting scenarios. The scenario you choose depends on the business question you’re trying to answer.

Classification Scenario
If you’re trying to determine the likelihood of whether or not something will happen, you’re dealing with a classification scenario.

For example, if your question is whether or not your customers will respond to a marketing offer, you can use a classification scenario to determine the probability of response for each of your prospective customers. This allows you to focus your efforts on targeting customers who are most likely to buy.

Regression Scenario
If you’re trying to predict a numerical value and explore the key drivers behind it you’re dealing with a regression scenario.

For example, if you want to predict the employee employment duration you would use a Smart Predict regression scenario. This will identify relationships in your data to help you better understand what factors lead to long-term employment. The result? These valuable insights can be used to influence your HR policies and reduce employee attrition.

Time Series Scenario
If you’re trying to forecast a future numerical value based on fluctuations over time, seasons, and other internal and external variables, you’re dealing with a time series scenario.

For example, a time-series predictive model can predict future sales volumes by analyzing historical sales data over time. This sales data, combined with additional information about your current sales force, marketing activities, or environmental factors like weather, can be used to project future performance trends.