1. Identify an output variable and a set of at least five input variables to test.
2. Use R, Python or other tool to compute regression model. This process should include selection of appropriate input variables (there should be at least two left, otherwise use another dataset).
   1. Description of the dataset.
   2. Explanation what is the real-world application of the ability to predict the chosen output variable.
   3. Description of the regression model and its results.
   4. Demonstrate how the model can be used to predict the output variable (use few examples from the dataset, plot the predicted value and the actual value). Discuss the model's accuracy.
   5. Explain the underlying nature of the computed regression model.

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