Geolocational Data Analyser

Classify Various Residential Places using REST API and Machine Learning

This Python Notebook aims to Classify various Residential places on any coordinate (Eg : Delhi) using K-Means Machine Learning Algorithm, Minisom and HERE REST API

Installation

Windows and Mac OS X and Linux :

pip install numpy
pip install matplotlib
pip install folium
pip install jupyter-notebook
pip install scikit-learn

Usage example

To run this all you need is a foursquare API Key which can be found Here

Development setup

To Setup the Notebook Install the Above Dependencies and setup your API Credentials as per HERE REST Documentation

Release History

• 0.0.6

  • Change the Datasource for very accurate clustering

• 0.0.5

  • Change the Algorithm for very accurate clustering (Minisom)

• 0.0.4

  • Fit Data using PCA to Plot much better clusters

• 0.0.3

  • Added PCA for variance analysis

• 0.0.2

  • Added Popups in Folium for a better idea of locations

• 0.0.1

  • Basic Skeleton is Ready, Need to add for Location as per User Input

Meta

PerfectoZ – Email – LinkedIn

Contributing

1. Fork it
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some New Feature')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request