Machine learning techniques have become integral to addressing complex challenges in economics and business. Different types of algorithms provide specific capabilities that are useful for various applications, from forecasting economic indicators and predicting customer churn to segmenting markets and assessing credit risk. This section covers four key machine learning techniques — regression, classification, clustering, and time series analysis — and how they are applied in economic and business contexts.
1. Regression for Forecasting
Regression models are foundational for predicting continuous outcomes based on various predictors. They are widely used in economics and business for tasks like forecasting financial metrics, estimating demand, and predicting market trends.
Applications:
- Economic Forecasting: Linear regression can predict economic indicators such as GDP, inflation, and unemployment rates by analyzing historical data.
- Revenue and Sales Prediction: Businesses use regression to estimate future sales or revenue based on factors like marketing expenditure, seasonality, and past sales.
Common Algorithms:
- Linear Regression: Assumes a linear…