Trifork Blog

Category ‘General’

Machine Learning: Predicting house prices

February 16th, 2017 by

Recently I have followed an online course on machine learning to understand the current hype better. As with any subject though, only practice makes perfect, so i was looking to apply this new knowledge.

While looking to sell my house I found that would be a nice opportunity: Check if the prices a real estate agents estimates are in line with what the data suggests.

Linear regression algorithm should be a nice algorithm here, this algorithm will try to find the best linear prediction (y = a + bx1 + cx2 ; y = prediction, x1,x2 = variables). So, for example, this algorithm can estimate a price per square meter floor space or price per square meter of garden. For a more detailed explanation, check out the wikipedia page.

In the Netherlands funda is the main website for selling your house, so I have started by collecting some data, I used data on the 50 houses closest to my house. I’ve excluded apartments to try and limit data to properties similar to my house. For each house I collected the advertised price, usable floor space, lot size, number of (bed)rooms, type of house (row-house, corner-house, or detached) and year of construction (..-1930, 1931-1940, 1941-1950, 1950-1960, etc). These are the (easily available) variables I expected would influence house price the most. Type of house is a categorical variable, to use that in regression I modelled them as several binary (0/1) variables.

As preparation, I checked for relations between the variables using correlation. This showed me that much of the collected data does not seem to affect price: Only the floor space, lot size and number of rooms showed a significant correlation with house price.

For the regression analysis, I only used the variables that had a significant correlation. Variables without correlation would not produce meaningful results anyway.

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Setting up PWM as a password recovery tool for OpenLDAP

July 28th, 2015 by

A running LDAP implementation without a password recovery service for users can be a real hassle for system administrators, in our case every time when a user forgets his/her password the only way to reset/change it was to go to the system administrator let him fix it.

As a solution for this problem we stumbled upon PWM as a password recovery service and in this blogpost will describe the steps you have to take to implement this yourself.

The reasons why we chose PWM as our service of choice are the following:

  • Open-source and still being actively developed.
  • It works with multiple LDAP implementations, including OpenLDAP.
  • Pretty intuitive design for the end-user.
  • A vast amount of configuration options, of which configuring our own password policy is one option.
  • Able to recover password by sending and Email/SMS token or PIN.
  • Captcha Integration with Google re-Captcha.
  • Event logs and statistics that are available to administrators.

The rest of this post will focus on walking through the installation and initial configuration of PWM with an OpenLDAP system. Most of the things we describe can also be found in the PWM administration guide or from other sources. However, some things (eg. configuration of certain modules in PWM) we didn’t immediately understand and we will describe some tips/solutions here.

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