The Rise of Artificial Intelligence

Data Science is reaching new levels and so are the models. But reaching a whooping 99.24% accuracy using simple feature engineering and a simple Decision Tree Classifier ? That's new! Hello everyone, today I am going to present you my model which can predict value range of a tender in Seattle Trade Permits with a whooping accuracy of 99.24 % (With some obvious caveats which I will discuss in the end). My Kernel : Yet Another Value Prediction The Prediction Kernel BASIC EDA This time out, I am going to use plotly library in Python. This is literally the best option for interactive plots and if you actually visit the kernel, you will understand why. First of all, we will focus on checking out the Top Grossing Contractors in the Seattle area who have earned the most out of the tender acquisitions. This will lead to this interactive graph: Similarly, one could plot out another graph for Amount earned per project. But another thing which caug...

Hey Readers,  As Python has gained a lot of traction in the recent years in Data Science industry. I wanted to outline some of its most useful libraries for data scientists and engineers, based on recent experience. NumPy When beginning to manage the scientific undertaking in Python, one unavoidably desires help to Python's SciPy Stack, which is an accumulation of programming particularly intended for scientific processing in Python (don't mistake for SciPy library, which is a piece of this stack, and the network around this stack). Along these lines we need to begin with a glance at it. Be that as it may, the stack is quite huge, there is in excess of twelve of libraries in it, and we need to put a point of convergence on the center bundles (especially the most fundamental ones).  The most major bundle, around which the scientific computation stack is constructed, is NumPy (remains for Numerical Python). It gives a plenitude of valuable highlights for tas...

Hey Readers, today I bring forth an exciting news for you all aspiring data scientists and machine learners! Something new happened in Microsoft Research Blog :  The Microsoft Research Outreach team has worked extensively with the external research community to enable adoption of cloud-based research infrastructure over the past few years. Through this process, we experienced the ubiquity of Jim Gray’s fourth paradigm of discovery based on data-intensive science – that is, almost all research projects have a data component to them. This data deluge also demonstrated a clear need for curated and meaningful datasets in the research community, not only in computer science but also in interdisciplinary and domain sciences. Today we are excited to launch  Microsoft Research Open Data  – a new data repository in the cloud dedicated to facilitating collaboration across the global research community. Microsoft Research Open Data, in a single, convenient, cloud-hosted ...

Hi readers ,  There are heaps of good reasons why researchers are so focused on model designs, however it means that there are not very many assets accessible to control individuals who are centered around deploying machine learning underway. To address that, An ongoing talk at the gathering was on "the preposterous adequacy of preparing information", and I need to develop that a bit in this blog entry, clarifying why information is so imperative alongside some commonsense tips on enhancing it. As a feature of my investigation I work intimately with a great deal of researchers and item groups, and my faith in the intensity of information changes originates from the gigantic additions I've seen them accomplish when they focus on that side of their model building. The greatest boundary to utilizing deep learning in many applications is getting sufficiently high accuracy in reality, and enhancing the preparation set is the quickest route I've seen to accuracy upgr...