Zillow-Home-Value-Prediction

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Zillow-Home-Value-Prediction

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XGBoost, LightGBM, LSTM, Linear Regression, Exploratory Data Analysis

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# Zillow-Home-Value-Prediction
Advance Data Sciences and Architecture

**Version 1.0.0**

Zillow is the leading real estate and rental marketplace dedicated to empowering consumers with full lifecycle of owning and living in a home- including homes for sale, homes for rent and homes not currently on the market, as well as Zestimate home values, Rent Zestimates and other home-related information. The Zestimate was created to give consumers as much information as possible about homes and the housing market, marking the first-time consumers had access to this type of home value information at no cost. “Zestimates” are estimated home values based on 7.5 million statistical and machine learning models that analyze hundreds of data points on each property.

The Main objective of this research project is to use different algorithms to make predictions about the future sale prices of homes, to reduce the error margin and increase the accuracy of Zestimate to get an exact valuation of homes.

**Installation**
You can find the installation documentation for the Jupyter platform, on ReadTheDocs. The documentation for advanced usage of Jupyter notebook can be found here.

For a local installation, make sure you have pip installed and run:

$ pip install notebook

Pre-requisities to run these python notebooks:
use the following syntax in Anaconda Prompt/ Command Prompt on your systems to import python libraries: 
pip install <library name>

**For someone who wants to run this notebook you need to install the following libraries in order to run the python notebooks:**
1. pandas
2. numpy
3. seaborn
4. matplotlib
5. keras
6. tensorflow
7. xgboost
8. lightgbm
9. sklearn
10. random
11. datetime
12. pylab
13. scipy
14. patsy

**Before Running any of my notebooks, kindly download datasets from the below url:**
https://www.kaggle.com/c/zillow-prize-1/data

**Guidelines to install xgboost are posted in the following url:**
http://xgboost.readthedocs.io/en/latest/build.html

**In order to run my project you need to run the notebooks in the following sequence:**
1. Run Project.ipynb
2. Run LinearRegression and Boosted Trees LightGBM.ipynb
3. Run Simple Neural Net.ipynb
4. Run LSTM Network.ipynb
5. Run Multivariate LSTM Network.ipynb

Licensed under the [MIT License](LICENSE)

**This MIT copyright license is only for the Linear Regression using Python**

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