Python代写|FinTech Python Assignment

这是一篇澳洲的python建模分析python作业

 

FinTech Use Case

Select only ONE “optimal” model designed to predict housing prices of real estate assets in a specific area given predefined asset and environmental characteristics (modified data is provided).

Specifically, your FinTech team members are interested to see the effect of “age” variable in addition to other variables on the mean observed price per unit. You are free to choose one relevant model (we have covered few ML variations starting from a base simple OLS). Your task is to explain your steps and results and show model coefficients and model accuracy, for example in mean square error (MSE) and/or R^2.

Model choices

  1. Linear Regression
  2. RANSAC Regressor
  3. LASSO
  4. Polynomials
  5. Decision Tree
  6. Random Forest

Your team members are keen to learn about python and would like to see and read the python script with simple explanations of your steps. This is in light of relevant FinTech news that ZILLOW is exiting home buying business. LINK

As a result, delivery is in one python.py script format as <studendtID_Name.py> with ‘’’<text>’’’ explanations of your steps. The aim is to describe your detailed steps from input variables to model parameters and provide final results in a critical, concise and logical way.

Recommended formats


程序代写代做C/C++/JAVA/安卓/PYTHON/留学生/PHP/APP开发/MATLAB


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