Hugh Brown hughdbrown

Chronic kidney disease

Description

Data source

UC Irving 400 rows by 25 attributes

Bit.ly data

Description

GermanWings crash/suicide news story spreads over bit.ly links.

Data source

bit.ly
twitter

Display style

Movie factors

Description

Movies are released and have varying levels of financial success. Can we figure out how successful a film will be by assessing its factors before its release?

Data sources

IMDB for movie particulars (genre, actors, director, etc.)
movie revenue source?

Real estate factors

Description

Real estate prices within an area can be predicted from past sales in an area with the addition of various factors:

number of bedrooms
number of bathrooms
square footage of house
square footage of lot

In addition, we might speculate that a couple of other factors that are not commonly recorded at at play:

Denver drivers

Description

Since arriving in Denver, I have noticed that Denver drivers do a whole range of semi-dangerous things on the road:

Changing lane with insufficient space
Lefthand turns in front of oncoming traffic
Running orange/red lights
Driving through totally red lights
Passing on the right on the highway

Projects

Selection

where

>>> import numpy as np
>>> a = np.array([[7, 4, 3, 7],
...  [7, 2, 5, 4],
...  [1, 7, 5, 1]])
>>> for (i, j) in zip(*np.where(a % 2 == 0)):
...     print("[{0}, {1}]: {2}".format(i, j, a[i, j]))
...

Day	Time	Total
Friday		2.00 hours
Saturday	10:00 - 18:00	8.00 hours
Saturday	19:30 - 21:30	2.00 hours
Sunday		4.00 hours
Monday	10:00 - 17:30	7.50 hours
Tuesday	09:00 - 17:00	8.00 hours

31.5 hours total

	from scapy.all import *

	AMAZON_TABLE = {
	'74:75:48:5f:99:30': 'Huggies,
	'10:ae:60:00:4d:f3': 'Elements',
	}

	def is_arp(arp):
	return (arp.op, arp.prc) == (1, '0.0.0.0')

	import ConfigParser
	import os.path


	def get_creds():
	botofile = os.path.expanduser("~/.boto")
	with open(botofile) as f:
	c = ConfigParser.ConfigParser()
	c.readfp(f)
	return tuple(