Regression analysis

Full Answer Section

• Data mining uses various techniques to extract knowledge from large datasets.
• It can involve tasks like data cleaning, preparation, and feature selection.

2. Regression Analysis:

• This is the statistical tool used to model the relationship between a dependent variable (what you want to predict) and one or more independent variables (what you think influences the dependent variable).
• It helps you understand how changes in the independent variable(s) affect the dependent variable.

1. Preparation: You start with a dataset containing the dependent variable (e.g., house price) and independent variables (e.g., square footage, number of bedrooms).
2. Model Building: Regression algorithms create a mathematical model that best fits the data points. For example, in linear regression, this might be a straight line equation.
3. Evaluation: You assess how well the model fits the data and predicts future values.

• Marketing: Predicting customer purchase behavior based on past purchases and demographics.
• Finance: Forecasting stock prices based on historical data and economic indicators.
• Healthcare: Estimating the risk of a patient developing a disease based on medical records.

• Prediction: Makes informed predictions about future outcomes.
• Understanding Relationships: Helps uncover how different factors influence a particular outcome.
• Decision Making: Provides valuable insights for data-driven decisions.

Sample Answer

Data mining regression analysis is a powerful technique used to uncover relationships between variables and make predictions based on those relationships. It falls under the umbrella of supervised machine learning, where you have a dataset with labeled data.

Here's a breakdown of the key aspects:

1. Data Mining:

• Imagine sifting through a giant mine of data, looking for hidden patterns and trends.