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# Zillow's Home Value Prediction

In this project, we are going to predict the log-error between their Zestimate and the actual sale price.
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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 either R or Python

## What will you get

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

## Prerequisites

• Jupyter Notebook from Anaconda installation
• R (3.3.3) and R-Studio (1.4) installation
• At least 4 GB RAM Machine

## 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) %u2212 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 project, we will develop an 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.