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Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
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.
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.
Data science project in R to develop automated methods for predicting the cost and severity of insurance claims.