This repository contains an end-to-end analysis and solution to the Kaggle house prices prediction competition.
The aim of this competition is to analyse 79 different features that describe every aspect of the residential homes in Ames, Iowa and subsequently make predictions on the final sale price of each home. This is an example of a regression problem in machine learning as sale price, which is our target variable, has a continuous distribution.
The key practice skills in this competition are:
- Creative feature engineering
- Advanced regression techniques like random forest and gradient boosting
Submissions are evaluated on Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sale prices.
Information regarding the columns in the dataset can be found in the data description text file as part of this repository.