How to do linear regression using Python and Scikit learn using one hot encoding?
<p>I am trying to use linear regression in combination with python and scikitlearn to answer the question "can user session lengths be predicted given user demographic information?"</p>

<p>I am using linear regression because the user session lengths are in milliseconds, which is continuous. I one hot encoded all of my categorical variables including gender, country, and age range.</p>

<p>I am not sure how to take into account my one hot encoding, or if I even need to.</p>

<p>Input Data:</p>

<p><a href="https://i.stack.imgur.com/gGnr7.png" rel="noreferrer"><img src="https://i.stack.imgur.com/gGnr7.png" alt="enter image description here"></a></p>

<p>I tried reading here: <a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html" rel="noreferrer">http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html</a></p>

<p>I understand the inputs is my main are whether to calculate a fit intercept, normalize, copy x (all boolean), and then n jobs.</p>

<p>I'm not sure what factors to take into account when deciding on these inputs. I'm also concerned whether my one hot encoding of the variables makes an impact.</p>