Advancing AI by tackling the most challenging prediction problems
  • AI-enabled capital market signals for hedge funds
  • High-value, business-critical predictions and recommendations for enterprise
Winners of the $1M 2019 Zestimates prize
We were part of the team that won the $1M Zillow Prize for creating the most accurate home valuation algorithm.

The algorithm won by a comfortable margin, beating more than 3800 teams from 91 countries.







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Our AI-enabled signals help hedge funds reduce risk
Hedge funds license Mindle capital market signals to improve performance by reducing return volatility, drawdown, and correlation with common factor return streams.

Mindle signals leverage hundreds of thousands of data points and sophisticated machine learning models.


Prize winner in the Two Sigma Financial Modeling Challenge
We were part of the 2nd place, prize winning team of the Two Sigma Financial Modeling Challenge against 2000 teams. The model searched for signal in financial markets data with limited hardware and computational time accurately predicting financial movements.
Our ML models help businesses increase the accuracy of their most important predictions
Across industries, there are certain predictions that, when more accurate, can completely transform your business. Those are the hard challenges we love to crack! And those are the problems where our team is proven to achieve the highest possible accuracy.

We help companies in industries like media, retail, ecommerce, and tech leverage every helpful data point to improve their most critical recommendations and predictions.


Prize winner in the 2018 ACM WSDM recommendation challenge
A Mindler was part of the prize winning team that ranked 2nd in the 2018 ACM Recommendation Challenge against over 1000 data science teams.

The algorithm helped Asia's leading music streaming service recommend more relevant music to its listeners.
Prize winner in the Home Depot Search Relevance Competition
A Mindler was part of the 2nd place, prize wining team in the Home Depot Product Search Relevance Kaggle competition against over 2000 data science teams. The algorithm helped them improve their customers' shopping experience by accurately predicting the relevance of search results.
Prize-winner in the Mercari Price Suggestion Challenge
A Mindler was part of the 2nd place, prize winning team of the Mercari Kagglle competition with 2000 competitors. Mercari, Japan's biggest community-powered shopping app, challenged data scientists to build an algorithm that automatically suggests the right product prices to sellers.
Prize Winner in the Rossmann Sales Forecasting Competition
A Mindler ranked 2nd and was a prize winner in the Rossmann Store Sales Kaggle competition against 3,738 data scientists. Rossmann operates over 3,000 drug stores in 7 European countries. Our model most accurately forecasted daily sales using historical sales data, product data, store data, and third party data such as weather and distance to competitors.
Mindle researchers in the news
"Shahbazi has competed in 20 Kaggle events, coming second five times and finishing in the top 15 another five times. He finished in the top five per cent of challengers on contests for everything from a Grupo Bimbo (the Mexican bakery multinational that owns Dempsters, Hostess, Vachon brands in Canada) inventory algorithm, a Home Depot online search relevancy challenge, a drug store sales volume prediction engine, a music recommendation system and even the 2017 Data Bowl that looked at lung-cancer detection".
Toronto man shares $1-million prize for real estate price predictions
"Zillow has slowly improved its Zestimate from a median error rate of 14 per cent when it started in 2006 to 5.7 per cent when the contest began in mid-2017. It's now down to 4.5 per cent nationally (it's higher in some cities and lower in others), and once the winners' tweaks to the algorithm are incorporated, the company expects the error rate to dip to about 4 per cent".
Online real-estate firm Zillow pays three guys, one from Toronto, $1M to improve home-value estimates

"Jordan Meyer of the United States, Chahhou Mohamed of Morocco, and Nima Shahbazi of Canada bested more than 3,800 teams representing 91 countries with an algorithm that beat Zillow's benchmark model (evaluated against real-time home sales between August and October 2018) by approximately 13 percent."
Meet the 'Zillow Prize' winners who get $1M and bragging rights for beating the Zestimate

"It's amazing to know that millions of people will benefit from our ideas," said Shahbazi, who competed as a team with Mohamed against Meyer in the contest's initial qualifier, but who decided to join forces with Meyer for the final round. "We brought every novel idea we could to our code and kept experimenting. For every idea that worked, there were a hundred that didn't work. But we kept going."
Zillow awards $1 million to team that reduced home valuation algorithm error to below 4%
On average, Zillow said, the Zestimate is $10,000 off the actual sale price for a median-priced home of about $223,900, and the information gleaned from the Zillow prize winnings could shave $1,300 off that discrepancy. It also moves the Zestimate's national median error rate below 4%.
Zillow's Zestimate got an upgrade — and this trio got $1 million for the new algorithm
"Drug store giant challenged Kagglers to forecast 6 weeks of daily sales for 1,115 stores located across Germany. The competition attracted 3,738 data scientists, making it our second most popular competition by participants ever.
Nima Shahbazi took second place in the competition, using his background in data mining to gain an edge. By fully exploring and understanding the dataset, Nima was able to engineer features that many participants overlooked."
Rossmann Store Sales, Winner's Interview: 2nd place, Nima Shahbazi
"Asked to search for signal in financial markets data with limited hardware and computational time, this competition attracted over 2000 competitors. In this winners' interview, 2nd place winners' Nima and Chahhou describe how paying close attention to unreliable engineered features was important to building a successful model."
Two Sigma Financial Modeling Challenge, Winner's Interview: 2nd Place, Nima Shahbazi, Chahhou Mohamed
"The winning team leveraged the power of NVIDIA TITAN Xp GPUs for both training and inference. For software, the team used the cuDNN-accelerated Keras, and TensorFlow deep learning frameworks".
NVIDIA GPUs Help Developers' Score $1 Million Prize For Improving Zillow's Zestimate
What if you could increase the accuracy of your most important predictions?
Contact us
If you want to make a prediction more accurate or if you are just curious what's possible, we'd love to help.

The more challenging, the better!

Contact krista@mindle.ai
778-710-0991

Unit 411, 4789 Yonge St,
North York, ON M2N 0G3
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