- We’ll pass balanced to the class_weight keyword argument of the LogisticRegression class, to get the algorithm to weight the foreclosures more to account for the difference in the counts of each class.
- Define a function to read in the acquisition data.
- We’ll use cross validation to make predictions.
- Train a model on groups 1 and 2 , and use the model to make predictions for group 3 .
- We’ll need to figure out an error metric, as well as how we want to evaluate our data.
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@kdnuggets: “Building a #DataScience Portfolio: #MachineLearning Project Part 3”
The final installment of this comprehensive overview on building an end-to-end data science portfolio project focuses on bringing it all together, and concludes the project quite nicely.
Building a Data Science Portfolio: Machine Learning Project Part 3