Hoping to win a machine learning competition in 2021? Here’s what you need to know. I collaborated with ML Contests, using their database of over 100 competitions that took place in 2020 across Kaggle, DrivenData, AICrowd, Zindi, and 20 other platforms. Wherever the information was available, we categorized winners to figure out what made them win.
Some of the highlights we’ll cover in this report:
Github recently introduced a new feature for Developers which allow you pin a markdown file (called special repository) to your GitHub profile, to let you describe more about your skill sets , what project you are working on and display a general overview of your portfolio.
Some of the interesting ones i have seen:
You know how competitions can be quite frustrating and strenuous especially when its a week before the deadline. You begin to refresh the page every time, looking at your rank. Over time, I have gotten used to it, and the most interesting part is you get to learn a lot of things aside chasing the prize I gained a lot of knowledge.
Recently, I took part in the zindi competition:
I was 5th and was placed at #5 (out of 164 teams) on the leaderboard.
In this blog post, I will summarise the main ideas of my solution.
ANALYTICAL HIERARCHY PROCESS FOR SELECTING THE BEST PRESIDENTIAL CANDIDATE
COURSE : MATHEMATICAL MODELING FOR ARTIFICIAL INTELLIGENT SYSTEMS
UNIVERSITY OF LAGOS (DEPARTMENT OF SYSTEMS ENGINEERING)
YOU CAN GET THE LINK TO MY CODES HERE
First of all what is AHP?
Analytic Hierarchy Process (AHP) is one of Multi Criteria decision making method that was originally developed by Prof. Thomas L. Saaty. In short, it is a method to derive ratio scales from paired comparisons. The input can be obtained from actual measurement such as price, weight etc., or from subjective opinion such as satisfaction feelings and preference. AHP allow some…
TEAM SOLUTION DEALING WITH REAL WORLD DATA AT DSN/ACCESS/AFF HACATHON
TEAM SOLUTION 2ND ON THE KAGGLE LEADER-BOARD.
THE TEAM CONSISTS OF 4 MEMBERS AND AN INSTRUCTOR FROM ACCESS BANK
WE HAVE OLALEYE ENIOLA
From my prior experience with LOAN DEFAULT PREDICTION, I’ve come to appreciate that this is one of the most complex problems for applying machine learning to. Data in this domain tends to be very heterogeneous, collected over different time frames, and coming from many different sources that may change and alter in midst of the data collection process. Coming up with a proper target variable is also…