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Python implementation of a case study of the MITProfessionalX course "Data Science: Data to insights"
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Prediction Wages-Ensemble Methods.ipynb
README.md
wage2015.csv

README.md

PredictingWages_EnsembleMethods

Python implementation of a case study in Module 2 of the MITProfessionalX course "Data Science: Data to insights".

The case study is: "Module 2 Case Study - Regression and prediction". This case study is about using ensemble methods in R on wages data.

Points of interest

Use different prediction methods (linear model, lasso, cross-validated lasso, random forest, Ridge, cross-validated Ridge, Elastic net, cross-validated Elastic net) and combine them using Ensemble method. Refer to sklearn.


Project description

Our goals are:

  1. Predict wages using various characteristics of workers.

  2. Assess the predictive performance of different methods (linear model, lasso, cross-validated lasso, random forest, Ridge, cross-validated Ridge, Elastic net, cross-validated Elastic net) and combine them using Ensemble method.

The data

Data is from the March Supplement of the U.S. Current Population Survey, year 2012.

The notebook

Linear Regression.ipynb


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