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This notebook is a study on the sales of newspapers of a local stand, with intention to predict the newspaper sales performance based on the different features available. For this, 4 sklearn models are applied: Linear Regression, Lasso Regression, Ridge Regression and Elastic Net Regression.

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isaqueiros/NewspaperSales-predictions-LinearRegression_and_Regularisation

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Newspaper Sales Predictions - Linear Regression and Regularisation Methods

This notebook is a study on the sales of newspapers of a local stand, with intention to predict the newspaper sales performance based on the different features available. For this, 4 sklearn models are applied: Linear Regression, Lasso Regression, Ridge Regression and Elastic Net Regression. The model is applied to dataset Newspaper_Sales, which is supplied separately in .csv format.

This file contains 4 main sessions:

  • Dataset Overview
  • Binary Linear Regression
  • Multiple Linear Regression
  • Regularisation Methods
    • L1 (Lasso)
    • L2 (Ridge)
    • Elastic Net
    • Evaluation of Regularisation Methods

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This notebook is a study on the sales of newspapers of a local stand, with intention to predict the newspaper sales performance based on the different features available. For this, 4 sklearn models are applied: Linear Regression, Lasso Regression, Ridge Regression and Elastic Net Regression.

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