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Problem statement analysis
Exploratory Data Analysis
Input Data Visualization
Interpretation from Visualization
Making sense of data
Implementation using R
Zillow is asking you to predict the log-error between their Zestimate and the actual sale price, given all the features of a home. The log error is defined as:
and it is recorded in the transactions file train.csv. In this project, you are going to predict the log error for the months in Fall 2017.
"Zestimates" are estimated home values based on 7.5 million statistical and machine learning models that analyze hundreds of data points on each property. And, by continually improving the median margin of error (from 14% at the onset to 5% today), Zillow has since become established as one of the largest, most trusted marketplaces for real estate information in the U.S. and a leading example of impactful machine learning.
In this data science project, we will develop a machine learning algorithm that makes predictions about the future sale prices of homes. We will also build a model to improve the Zestimate residual error. And finally, we'll build a home valuation algorithm from the ground up, using external data sources.
