# Zillow’s Home Value Prediction (Zestimate)

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

## 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) %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 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