california_housing_prices documentation!

This report analyzes the Random Forrest regression model for California housing prices. (A work in progress)

Summary

The model yielded an R^2 value of .83

Code on github

Contents:

• California Housing Prices README
  • Data Publishers
  • Project Organization
• Commands
  • Data scripts
  • Model scripts
• Python Modules
  • features.build_features

Analysis

Jupyter notebook contents:

• Project Goals
• Get the data
  • Split the data
  • reduce the number of categories
  • Stratify data
  • Save strata to pickles
• Explore data (gain insights)
  • Experiment with Attribute Combinations
• Prepare data for Machine Learning algorithms
  • Explore missing data
  • Three options to handle missing data
• Explore models
  • Linear Regression
  • Decision Tree
• Fine tune model
  • Decision Tree Regression
  • Linear Regression
  • Random Forrest Regression
  • Support Vector Machines (linear kernel)
  • Grid Search
  • Evaluate Test data

Indices and tables

• Index
• Module Index
• Search Page