Building a regression model generally involves the following steps:
Data Collection: Gather relevant data for the dependent and independent variables. Data Preprocessing: Clean and prepare the data for analysis. Model Selection: Choose the appropriate regression model based on the data and business problem. Model Training: Use statistical software or programming languages like R or Python to train the model. Model Evaluation: Assess the model's performance using metrics like R-squared and Mean Squared Error (MSE). Model Deployment: Implement the model for real-time decision-making.