About

This repo contains various analysis on different datasets. Current analysis focuses on time series forecasting and anomaly detection.

Datasets

Wikipedia

Drawing graph of page links

import urllib3
import networkx as nx
from wikipedia.parser import get_graph

pool = urllib3.PoolManager()

G = get_graph(pool, url = "https://en.wikipedia.org/wiki/Data_mining", deep=1)
nx.draw(G, nx.circular_layout(G), with_labels=True)

Finding philosophy page

In this experiment, I'll test the hypothesis that: By going to the first link on any Wikipedia article, you'll end up on the
philosophy article.

For more info on the subject go to my dev.to article.

crawl(pool, "https://en.wikipedia.org/wiki/Data_mining", phrase="Philosophy", deep=30, n=1, verbose=True)

30 Entering Data_mining
29 Entering Data_set

...

   [('https://en.wikipedia.org/wiki/Thought',
     [('https://en.wikipedia.org/wiki/Ideas',
       ['https://en.wikipedia.org/wiki/Philosophy'])])])])])])])])])])])])])])])])])])])])])])])])])])

Experiment and code

E-commerce dataset from brazilian retail store

Dataset - sampled daily Prophet prediction of order volume with confidence intervals

Notebooks:

• Predictions with prophet
• Preparing data
• Exploring data as time series
• Frequent pattern mining with fpgrowth

Animations:

Smoothed with 3-day moving average, yearly seasonality

User interactions database

This dataset contains data from a news website. Each csv file contains info about sessions, clicks on articles, time of interaction etc.

In file frequency_analysis.ipynb the distribution of page on with the article appears is analysed. For any session I added the order of articles. Then I filtered for that one article and created histograms for each hour.

Image processing

Animation of how Sobel edge detetion works: