Reasearch project on clustering clients from transaction history
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
Run the following to install:
pip install geot_cluster’’’
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
import geot_cluster import markov_clustering as mc import matplotlib.pyplot as plt import networkx as nx path = [path to file with data] data, names = geot_cluster.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): geot_cluster.archivate_maps(data, names, levels=4) if(libs): lib = geot_cluster.graph_preparation(data, names, base) prob_lib = geot_cluster.znakomstvo_by_lib(lib,data) lib, prob_lib = load_libs(base = base) if(graph_f): graph = geot_cluster.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)
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