Zillow’s Home Value Prediction (Zestimate)

Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.

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What will you learn

  • Problem statement analysis
  • Exploratory Data Analysis
  • Input Data Visualization
  • Interpretation from Visualization
  • Making sense of data
  • Implementation using R

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Project Description

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:

logerror = log(Zestimate) − log(SalePrice)- log(SalePrice)

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.

Curriculum For This Mini Project

 
  Problem Statement
01m
  Explore Data Set
02m
  Understand the features
03m
  Import Libraries
03m
  Recoding of variables
04m
  Find transactions by month
12m
  Distribution of Transactions
01m
  Distribution of Target variable
15m
  Represent Missing values
07m
  Finding relevant features
02m
  Correlation between features and target variable
14m
  Shape of Distribution
04m
  Spread of log error over years
04m
  Zestimate variable prediction
06m
  Building Model
10m
  XGBoost Model
13m
  Prediction
04m
  Hyperparameter Tuning
01m
  Cross Validation
03m
  Get Best Results
16m
  Conclusion
01m