Predicting Housing Prices with Linear Regression

In this Post I will go through the workflow of a full machine learning project with the Ames housing dataset, using Linear Regression. This post was initially created with Jupyter Notebook. Unfortunately with WordPress, it is only possible to display a Jupyter Notebook in a small window, like you can see below. Therefore I would recommend you to view it on github, because it is better displayed there.

Table of Contents:

  • Part 1: Data Exploration
  • Part 2: Missing Data
  • Part 3: Out liars
  • Part 4: Kurtosis, Skewedness etc.
  • Part 5: Deleting Categorical Features
  • Part 6: Converting Categorical features
  • Part 7: repeat feature-engineering at test_df
  • Part 8: Train & Test the Model


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