jlbrosnahan / Predict-House-Prices

Regression problem predicting Boston house prices in RStudio

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Predict House Prices

Predicting Boston house prices in RStudio

Background

We have been asked to investigate the Boston House Price dataset. Each record in the database describes a Boston suburb or town.

Business Problem

Can a model be built to predict house prices in Boston Area with 80% accuracy level?

Data Description

The data was drawn from the Boston Standard Metropolitan Statistical Area (SMSA) in 1970 from UCI Machine Learning Library.

Machine Learning Skills

  • Comprehensive univariate and multivariate plots
  • Linear algorithms: linear regression, logistic regression
  • Non-linear algorithms: Support Vector Machines (radial basis), CART, KNN
  • Ensemble algorithms: Stochastic Gradient Boosting, Random Forest, Cubist
  • Resample accuracy comparison and plots
  • Model tuning, grid search
  • Business problem solved

Summary

House prices can be predicted with 90% accuracy using top Cubist tuned algorithm. The error rate of our top model is plus or minus $3.2 thousand. Business objective has been achieved.

Predictions of Top 10 Rows on New Dataset

Visual 1

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Regression problem predicting Boston house prices in RStudio


Languages

Language:R 100.0%