DataScienceAPI

This is a RESTful API built using Flask and Scikit-Learn. It provides a host of Classification and Regression algorithms that can be used readily and returns results in the form of predictions, confusion matrices, accuracy scores and more.

Description

• /rfclassification: Classification using Random Forest algorithm.
• /rfregression: Regression using Random Forest algorithm.
• /svmclassification: Classification using Support Vector Machines algorithm.
• /knnclassification: Classification using K-Nearest Neighbor algorithm.
• /dtclassification: Classification using Decision Trees algorithm.
• /svmregression: Regression using Support Vector Machines algorithm.
• /dtregression: Regression using Decision Trees algorithm.
• /knnregression: Regression using K-Nearest Neighbor algorithm.
• /gnbclassification: Classification using Naive Bayes(Gaussian) algorithm.
• /bnbclassification: Classification using Naive Bayes(Bernoulli) algorithm.
• /logisticregression: Classification using Logistic Regression algorithm.

To Run

• Clone into repo
• Type in pip install (preferably inside a virtual environment)
• Then run python3 main.py
• Use a REST client to make post requests to the Flask Server

Two sample datasets and the request format are included to test out the API.