geot-cluster 0.9.1

Third course reasearch project on developing the way to cluster bank clients from date, time and coordinates in their transaction history

# Geographical Transctions clustering algorithm

Name of the module stands for geographical transactions clustering. This module is an implementation of the method, developed for the third course project in HSE University. It takes dataframe with clients transactions history of the specified format and returns list of clusters.

For the record, it was intended to be for public usage in this form, as it is a research project seeking to find a way to deal with the described problem

## Installation

Run the following to install:

‘’’python

pip install geot_cluster’’’

## Usage

Before using make sure, that your dataset corresponds with requirements. Csv file must contain the following columns in order to work correctly

• user_id : string type, example: “423156821”

• event_dt : string type, example: “20190312”

• event_time: string type, example: “2019-03-12 06:24:00.279”

• lattitude : float type, example: 49.862621

• longtitude: see lattitude

‘’’python

import pandas as pd import numpy as np import markov_clustering as mc import matplotlib.pyplot as plt import math import pytz import folium import os.path import networkx as nx

from haversine import haversine, Unit from collections import Counter from datetime import datetime from timezonefinder import TimezoneFinder from IPython.display import clear_output

import geotrans_cluster

path = [path to file with data] data, names = data_load(path)

%matplotlib notebook base = [path to the folder, where to store libs with information about clients]

archivate = True libs= True graph_f = True cluster_f = True

if(archivate):

archivate_maps(data, names, levels=4)

if(libs):

lib = graph_preparation(data, names, base) prob_lib = znakomstvo_by_lib(lib,data)

lib, prob_lib = load_libs(base = base)

if(graph_f):

graph = graph_forming(lib, prob_lib, treshold=0.9)

if(cluster_f):

result = mc.run_mcl(graph,pruning_threshold=0.7, inflation=2,expansion=2) clusters = mc.get_clusters(result)

clust_0 = clusters_to_ids(lib=lib, prob_lib=prob_lib, clusters = clusters, number = 0) maps = get_cluster_maps(data = data, clust = clust_0) print(“Number of clusters”, len(clusters))

plt.figure(figsize=(10,10)) mc.drawing.draw_graph(result, clusters, edge_color=”red”,node_size=15,width = 1, with_labels=True, font_size = 8)