We present various important features to use while predicting housing prices with good accuracy. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. While in Neural Networks, solely the difference between the predicted and existing values determines the change in weights, hence determining the best fit.
Goal : To compare the accuracy of prediction of house prices using XGBoost vs Structural Neural Networks and implement the better of the two in a website :)
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
What things you need to install the software and how to install them
Hardware Requirements
• Processor: 2GHz or faster processor
• RAM: 2GB(64bit)
• Storage: 5GB of available hard disk space
• Consistent internet connection having speed of 512Kbps at a bare minimum.
• Other general hardwares such as a mouse and keyboard for inputs and a monitor for display.
Software Requirements
• Operating system: Linux or Windows
• Programming languages: Python
• Anaconda (Open Source Python Distribution) link : https://www.anaconda.com/distribution/
Navigate to the project root directory in the anaconda prompt shell
Install all the required libraries. Type the following command :
pip install -r requirements.txt
Run the script :
python app/start.py
Open a web browser and navigate to localhost:5000
Since the scripts are too heavy and the training takes a long time, you will have to wait a good couple minutes to see the results. What you will see in front of you is the website of PES Real Estate. In the box of "Get going!", type in your lot area, no. of bedrooms and year built, and click "Predict". The page will reload (for a long time) and you will have your answer in the "Cost" textbox.