Solutions to "Just KNIME It!" Challenge 3 - Season 3

:sun_with_face: Happy Wednesday, folks! We just posted a fresh Just KNIME It! challenge. :sun_with_face:

:house_with_garden: This week you’ll wear the hat of a real estate agent who just moved to a new city. Before directly working with potential buyers, she decided to first dive deep into the region’s data to better understand the market. :money_with_wings: What are her main insights?

Here is the challenge. Let’s use this thread to post our solutions to it, which should be uploaded to your public KNIME Hub spaces with tag JKISeason3-3.

:sos: Need help with tags? To add tag JKISeason3-3 to your workflow, go to the description panel in KNIME Analytics Platform, click the pencil to edit it, and you will see the option for adding tags right there. :slight_smile: Let us know if you have any problems!



PRICE sqft_living sqft_above bathrooms sqft_basement bedrooms yr_renovated sqft_lot yr_built
Number (double) Number (double) Number (double) Number (double) Number (double) Number (do Number (double) Number (double)
0.702034604 0.605567041 0.525136322 0.323815568 0.308376918 0.126433672 0.089660681 0.054010941

The Corelation of Price to other features in the decreasing order is given alongside


Nothing very fancy, but I think it addresses the challenge.


HI @alinebessa :wave: :slightly_smiling_face:

This afternoon Jakarta time, I have completed the solution to the given challenge. In tackling this challenge, I took on the role of a data analyst working at a real estate company. My solution can be accessed through this link, JKISeason3-3 arief_rama – KNIME Community Hub


Hello everyone, this is my workflow.


2. The distribution of prices

3. The correlation between housing prices and other features

Most customers may prefer the features at the top/front


Hi there,

Here is my workflow:

Some screenshots:

Thank you!



Hi all,
Here is my solution.

I have created an interactive dashboard to check the correlation between price and housing features. The scatter plot shown below was created using Plotly and scikit-learn.


Hi all,
Here is my solution. Very simple one.


Hi all,
Here is my solution. I built a predictive model of housing prices and analyzed the feature importance.


:boom:Excellent, in my opinion, this is exactly the approach I was thinking of to tackle this challenge: Predict House Price ==> Features Importance.

1 Like

Find herewith my submission for the challenge.


This is my solution


Hi all,
Here’s my solution, similar approach to @sryu. Random forest regression was used simply with default settings. The global feature importance shows that zipcode and sqft_living contribute to housing prices significantly.


A little bit just want to spend my free time exploring more about KNIME after finishing my assignments from the university.


Hi all, here is my solution.

1 Like

Here’s my solution to this weeks challenge

  • extracting overall averages for price, living space and lot size using statistics view node
  • finding correlated features using Linear Correlation Node and plotting them as scatter plots
  • Visualization of averages by ZIP code on a map using Geospatial Analytics Extension

House Prices JKI3-3 Leo_Wynter
Interesting challenge.
Got to try out some EDA followed by a bit of some machine learning (barebones, not much fixing)
Used the Statistics View for summary statistics, a histogram plot and Plotly’s violin plot node for checking price distribution as well as a heatmap for correlation.
Also played around with Palladian Map viewer (these homes were in Seattle?)
Had fun!!


Oh guys this was a fun one!! Here is my solution:

I built a data app that, when first opened shows some filters in the form of range sliders, a button to visualize the houses you select and a data table of the different real estate listings:

When you use the ranger sliders to narrow the listings, the table below is automatically updated. You can then sort using the table and finally, using the check boxes, select a few that you are interested in learning more about:

Now you hit the “Visualize Selected Houses” button and presto! A map is displayed showing you the location of each house you selected from the table (blue arrows)!

If you zoom in on one of the listings, you see that there is more information available. For each house selected, the Haversine distance to all other listings is calculated and anything within a certain threshold is also shown on the map. Additionally, the OSM point of interest nodes were used to add schools and fuel stations to the map, since these are possible amenities one would consider in buying a house. Others could be added!!

Finally, when you hover over any listing, a pivoted table is displayed with more of the property information.

Checkmate Zillow!

Hope you enjoy fellow KNIMErs!


Just a barebones analysis of the data and the statistics to get a better sense of the data that is being dealt with.


Here is mine:

Enriched with GeoJSON, for the ZIP Codes, from

  1. High Level Property Analysis
    Impact of Condition, View and Grade to Price and Count of Sales

  2. Geo-heat-Map Overview
    Population Density, Count of Sales and Price (mean) per ZIP Code

  3. Waterfront Impact

  4. Condition vs. View for Sales Count and Price (mean)

PS: I noticed that I missed updating the Insights which I will do that later today.

Full Reports

240604-Season-3-Challenge3-Real Estate Analytics-report.pdf (483.4 KB)

Solution Workflow