The Rise of Artificial Intelligence

Hello Readers! Welcome to yet another value prediction work! Today, we will be looking at the in-demand dataset , namely Genetic Variant Classifications . We will look at this dataset and go for it's primary objective, that is classification of  the two lab reports and determining whether they both conflict or not. The Kernel you may want to look at for more information : Conflicting result classifications As usual, we will be looking at the dataset with the aim of EDA , Feature Engineering and Predictions Exploratory Data Analysis One would like to see what are the Chromosomes vs Class distribution of this data. For that, you can simply use :  As you can observe in the graph given below, the dataset happens to be heavily biased towards the  non- conflicting  genes and that too with the  CHROM == 2  standing out as the clear bias winner. Since the incidents where the genes are recorded to be  conflict...

Hey readers! Today, allow me to present you yet another dataset analysis of a rather gluttony topic, namely Avocado price analysis. This Data set  represents the historical data on avocado prices and sales volume in multiple US markets. Our prime objectives will be to visualize the dataset, pre-process it and ultimately test multiple sklearn classifiers to checkout which one gives us the best confidence and accuracy for our Avocado's Organic assurance! Note : I'd like to extend the kernel contribution to Shivam Negi . All this code belongs to him. Data Visualization This script must procure the following jointplot  While a similar joint plot can be drawn for conluding the linearly exponent relations between extra large bags and the small ones. Pre Processing The following script has been used for pre processing the input data. Model Definition and Comparisons We will be looking mostly at three different models, namely ra...

Hey guys! I recently came across one excellent poetry algorithm named Deep-Speare.  It was developed by   A four-person team from the School of Information and the Graduate School of Education designed a computer algorithm   which was able to successfully fool people  trying to distinguish between human- and bot-written verses nearly 50 per cent of the time.  However, experts could still easily identify machine-generated poetry, and AI may have a long way to go before it can outdo Shakespeare, researchers said.  Computer scientists at University of Melbourne in Australia and University of Toronto in Canada designed an algorithm that writes poetry following the rules of rhyme and metre.  In some ways, the computer's verses were better than Shakespeare's. The rhymes and metre in the machine-generated poetry were more precise than in human-written poems. Test Run Feedback  The following excerpt is courtesy Economic Times: ...

Hey there! My dear readers.  Today this kernel review is going to be about the famous MNIST data-set, one of the most famous introductory datasets which we encounter along side Iris dataset and the titanic survival challenge data sets.  Since it is a competition kernel, I have decided not to make it public. (yet) Also,  if you want to try your hands at the challenge itself, then you can find the challenge page here :  Note: This kernel has been largely focused on network modelling rather than Exploratory Data Analysis because it's simple, classic stuff. Still, I will try my best to explain that stuff here. Exploratory Data Analysis First indication of a great dataset is the face that it gives all the mentioned labels equal rows in it and this one doesn't disappoint. This data set maintains a fair 4000+ entries per label which actually is a great statistic for a good dataset. And the next thing one needs to know is how...

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 ...

Hey Folks! I recently came across an excellent hot trending dataset in kaggle. It is named Mobile App Statistics (mainly based upon Apple iStore) and instantly determined to try my hands on this data set. Link to the data set: Apple App Store (7200 datasets) Now, our objectives would be these 3 for the developed kernel : Checking out popular applications in each genre in Apple Store (Basically, grabbing the top charts for our data Analysis)  Checking the trend of an App's  User Experience  with respect to its  cost ,  User Rating count  and  Number of devices and Languages  it supports. Judging a game's popularity by its APK size and make a  Random Forest Classifier  to classify by popularity Of these 3, I will be discussing the first and the third objective here! Before anything else, one may notice that the size in bytes is not really a good standard of measure, so why not convert it into Megabytes by th...