title: How to forecast in python using machine learning , from a given set of geographical data?
body: <p>I was analyzing some geographical data and attempting to predict/forecast next occurrence of event with respect to time and it geographical position. The data was in following order (with sample data)</p>
  
  <pre>
  Timestamp Latitude     Longitude   Event 
  13307266  102.86400972 70.64039541 "Event A"
  13311695  102.8082912  70.47394645 "Event A"
  13314940  102.82240522 70.6308513  "Event A"
  13318949  102.83402128 70.64103035 "Event A"
  13334397  102.84726242 70.66790352 "Event A"
  </pre>
  
  <p>First step was classifying it into 100 zones, so that reduces dimensions and complexity.</p>
  
  <pre> 
  Timestamp Zone 
  13307266  47
  13311695  65
  13314940  51
  13318949  46
  13334397  26
  </pre>
  
  <p>Next step was to do time series analysis then I got stuck here for 2 months, read around a lot of literature and figured these were my options
  * ARIMA (auto-regression method)
  * Machine Learning</p>
  
  <p>I wanted to utilize Machine learning to forecast using python but couldn't really figure out how.Specifically are there any python libraries/open-source-code specific for use case, which I can build upon.</p>
  
  <p>EDIT 1:
  To clarify, data is loosely dependent on past data but over a period of time is uniformly distributed.
  The best way to visualize the data would be, to imagine N number of agents controlled by a <em>algorithm</em> which allots them task of picking resource from grids. Resources are function of socioeconomic structure of society and also strongly dependent on geography. Its in interest of " <em>algorithm</em> " to be able to predict demand zone and time wise.</p>
  
  <p>p.s:
  For Auto-regressive models like ARIMA Python already has a library <a href="http://pypi.python.org/pypi/statsmodels" rel="noreferrer">http://pypi.python.org/pypi/statsmodels</a> .</p>