Data Set ,Machine Learning Problem : Quora question pair similarity – II
In the previous post, I explained all the necessary details, from business impact to performance metric, etc. We ended the post by uploading all the libraries. Time to upload the data into our system. We will use pandas read.csv function and some other functions like info and shape to get some insights into various details…
Data Set ,Machine Learning Problem : Quora question pair similarity – I
In this blog, I would like to take you through the process of how to go about solving a dataset or a data science problem. Quora Question pair was actually a competition hosted on kaggle with decent prize money and anybody in the world could participate in it. But the competition is long over and…
Virtualenv, Text IDE’s,Pytest,Tox
Virtual Environments Before we come to it. We heard the term virtual reality, it is like the real world like experience, similar in all respects or close to similar. We have seen in the video games mostly. Virtual environments are mostly similar in understanding. It is the environment created to try to carry out different projects which require different settings.…
Git and Github – Nomad’s Nest
What has nature got to do with this? Well, everything is connected Abhijeet, said God in his deep baritone. (I always thought God would have a voice like Morgan Freeman or James Earl Jones). Anyway, like sparrows go out to collect their food, come back to their nest, each one of the inhabitants contributing their share. To do what, make a…
Paradigms of python programming- programming fads
Ever thought that you can associate a term like “style” to a programming language, well actually, you can associate so many of these fashion terms. But first let me tell you more about the python language, before I interest you on this idea. Admittedly, Python is my favorite language considering my limited knowledge and understanding…
Scripts ,YAML and Data Science – Deployment Utility.
The idea of this post is to clarify that we don’t use the actual notebook (which we use at the research stage and build the model), as our production code. Then what do we use and why? A pipeline is a set of data processing steps connected in series, where typically, the output of one…
Production and Deployment of Machine Learning Models – III
Architecture Framework implemented to carry out an end to end prediction. In order to establish an out-and-out machine learning system, where we get the data, train our model and then deploy the model to serve customers we need a suitable designed architecture. We should straightway explore few important design architectures. We will touch upon all…
Deployment and Productionization of Machine Learning Models – From Acme to Nadir – II
Bricks and concrete that builds a machine learning home: System and Architecture. A Machine Learning system is an entity that comprises the whole infrastructure(infrastructure that includes all the hardware from RAM to normal platform building equipment), then it includes operating system that power our machine learning task and makes it simpler by providing a robust…
Outliers in Data, Emphasis in Data Science.
Outlier – The Enemy under wraps Outlier: I always like to think an outlier as Shashi Tharoor placed between general English-speaking group. He would really stand out, in the way he speaks English.Well, Shashi Tharoor will be an outlier in most groups. Outlier is a point or observation that differs significantly from the from the rest of…
Deployment and Productionization of Machine Learning Models – From Acme to Nadir – I
Data Science – the business affair The typical process of building a machine learning model involves data collection: the process used to get the data from the databases(ETL) or from any other source, then Analyzing the data, engineering and selecting features from the data to building a model on the data, and lastly analyzing model…
Missing Values – How to treat them in data.
Missing Values Missing Value means an absence of observation in a variable. A Table consists of rows and columns, each column represents a distinct variable. Missing value/values mean an observation /observations is /are missing or misrepresented in a column. for Example, your dataset has 5 columns and 100 rows. In some of the columns, you…
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