Common rule in machine learning is to try simple things first. For predicting continuous variables there's nothing more basic than simple linear regression. "Simple" in the name means that there's only one predictor variable used (+ intercept, of course):
y = b0 + x*b1
where b0 is an intercept and b1 is a slope. For example, you may want to predict lemonade consumption in a park based on temperature:
cons = b0 + temp * b1
Temperature is in well-defined continuous variable. But if we talk about something more abstract like "weather", then it's harder to understand how we measure and encode it. It's ok if we say that the weather takes values {terrible, bad, normal, good, excellent} and assign values numbers from -2 to +2 (implying that "excellent" weather is twice as good as "good"). But what if the weather is given by words {shiny, rainy, cool, ...}? We can't give an order to these variables. We call such variables categorical. Since there's no natural order between different categories, we can't encode them as a single numerical variable (and linear regression expects numbers only), but we can use so-called dummy encoding: instead of a single variable weather we use 3 variables - [weather_shiny, weather_rainy, weather_cool], only one of which can take value 1, and others should take value 0. In fact, we will have to drop one variable because of collinearity. So model for predicting traffic from weather may look like this:
traffic = b0 + weather_shiny * b1 + weather_rainy * b2 # weather_cool dropped
where either b1 or b2 is 1, or both are 0.
Note that you can also encounter non-linear dependency between predictor and predicted variables (you can easily check it by plotting (x,y) pairs). Simplest way to deal with it without refusing linear model is to use polynomial features - simply add polynomials of your feature as new features. E.g. for temperature example (for dummy variables it doesn't make sense, cause 1^n and 0^n are still 1 and 0 for any n):
traffic = b0 + temp * b1 + temp^2 * b2 [+ temp^3 * b3 + ...]
