Price Prediction API

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Clone repository

git clone https://github.com/AndranikUgujyan/price_prediction_task.git

Install and create virtual environment

pip install virtualenv

virtualenv venv

Activate a virtual environment

source venv/bin/activate

Install required packages

pip3 install -r requirements.txt

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Data:

To preprocess and split dataset into train/test run:

python -m price_model.data_proc.processor

The command will generate normal sampled datasets and will save as a csv files inside data folder.

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Models:

• Model 1: Random Forest Regressor
• Model 2: XGB Regressor

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Model Train and Save

For train and save model based on normalized data run:

python -m price_model.training.train

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Comparing the performance of each of models

Model's performances

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Run API

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We have 2 option to run API:

1. without docker
2. inside docker

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Run API without docker

 python3 -m price_model.app

Test API without docker

 curl  -X POST -d '{"data": [{"brand":"Samsung","RAM_GB":1000,"GHz":3.2, "HDD_GB":1000,"model":"rf"}]}' http://127.0.0.1:8080/predict_price -H "Content-Type:application/json"

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Run API inside docker docker

Build docker image

docker build -t predict-price-api:1.0 -f Dockerfile .

Run docker

docker run -e PORT=8080 --name imbd -p "7894:8080" predict-price-api:1.0

Test API in docker

curl  -X POST -d '{"data": [{"brand":"Samsung","RAM_GB":1000,"GHz":3.2, "HDD_GB":1000,"model":"rf"}]}'  http://127.0.0.1:7894/predict_price -H "Content-Type:application/json"

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Here is an example response of predict_price prediction endpoint:

{
 "price": 81981
}