End-to-End Light-Weight Machine learning model deployment (Using Statistics, Python, Streamlit, Heroku, and Github)

Introduction

The Scenario :

Objectives

Uniqueness of Project

Data Collection and Description

Histogram distribution of rent prices in some locations showing higher prices indicating that such houses are likely to be in an estate / Serviced but never reported in the description columns.
Median house prices Specific Location like Gbagada, Surulere, Yaba.

Data Modelling

from sklearn.externals import joblib
joblib.dump(model1, 'modelrf.pkl')
modelza= joblib.load('modelrf.pkl')
pip install pipreqspipreqs ./
streamlit==0.58.0
matplotlib==3.1.3
joblib==0.14.1
numpy==1.18.1
pandas==1.0.1
scikit-learn==0.22.1
web: sh setup.sh && streamlit run housing2.py
mkdir -p ~/.streamlit/ 
echo
"\[server]\n\
headless = true\n\
port = $PORT\n\
enableCORS = false\n\
\n\
" > ~/.streamlit/config.toml
Final End Product

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Industrial Engineer with interests in Machine learning/Robotics/IOT

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Ayanlowo Babatunde

Ayanlowo Babatunde

Industrial Engineer with interests in Machine learning/Robotics/IOT

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