Python Regression & Forecasting: Data Science Guide

Linear Regression: Use when you expect a linear relationship between the independent and dependent variables. Polynomial Regression: Suitable when the relationship appears to be polynomial, like quadratic or cubic. Lasso or Ridge Regression: Helpful when dealing with multicollinearity or to prevent overfitting in high-dimensional datasets.

Mean Absolute Error (MAE): Measures the average absolute differences between predicted and actual values. Mean Squared Error (MSE): Calculates the average of squared differences between predicted and actual values. Root Mean Squared Error (RMSE): The square root of MSE, providing a more interpretable error metric.

Data Preprocessing: Clean and preprocess your time series data, handle missing values, and ensure it's in a suitable format (e.g., pandas DataFrame). Train-Test Split: Split your data into training and testing sets to evaluate model performance. Feature Engineering: Create relevant features, such as lag values, rolling statistics, and seasonality indicators. Model Selection: Experiment with different forecasting models, such as ARIMA, Exponential Smoothing, or machine learning models, based on your data characteristics.

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