Exposing Machine Learning Model As REST API

#MachineLearnin #DataScienc #Pytho #ProofOfConcept #PO #ArtificialIntelligen #A #EndToEn #machinelearnin #REST API#Model Deployment#

As part of this Proof-Of-Concept, we're exposing Machine Learning model as an API and accessing via postman.

Includes below steps:
i) Save the model as pickle file
ii) Load the model to get prediction using test data
iii) Flask based API set up
iv) Simulate the input data using Postman
v) Get the predicted output from the model
vi) Summary

Required Tools:
a) Google Colab
b) PyCharm (or any python editor)
c) Postman

Notebook Link: https://localsfriend.com/courses/linearregression/

Dataset Attribute Details: Boston Housing Dataset
CRIM – per capita crime rate by town
ZN – proportion of residential land zoned for lots over 25,000 sq.ft
INDUS – proportion of non-retail business acres per town
CHAS – Charles River dummy variable (1 if tract bounds river; else 0)
NOX – nitric oxides concentration (parts per 10 million)
RM – average number of rooms per dwelling
AGE – proportion of owner-occupied units built prior to 1940
DIS – weighted distances to five Boston employment centres
RAD – index of accessibility to radial highways
TAX – full-value property-tax rate per $10,000
PT – pupil-teacher ratio by town
B – 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
LSTAT – % lower status of the population
MV – Median value of owner-occupied homes in $1000’s