/londonhousingprice

London Housing Price model trainer and inference api

Primary LanguagePython

London House Price prediction Demo project

Note: this project is intended for pipeline generation for training and inference

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##Building Docker images

To build Trainer

  • Rename Dockerfile_Trainer to Dockerfile
  • Run in interactive environment
  • docker run -it -v /yourworkdir:/tf/workdir -w /tf/workdir -p 8888:8888 IMAGEID bash

To build default DNN model just run

python train.py
If you need to fetch new data from GCP you need to provide credential file python train.py --credential_file credential_file.json

To build Inference API

  • Rename Dockerfile_Inference to Dockerfile
  • Run image which serves model
  • docker run -d -p 8080:8080 IMAGEID
  • use localhost:8080 to access serving api Api docs and sample can be found in localhost:8080/apidocs

Basic EDA

Seasonality

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Area effect on price

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Current Model performance

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