To perform the EDA visualization gives a quick glance and understanding of independent variables. On further deep diving from univariate to multivariate analysis can be easily understood by visualization.

So here is the new library autoviz which gives an enhanced visualization experience in a few codes of lines.

Installing

!pip install autoviz

Initializing the class

from autoviz.AutoViz_Class import AutoViz_Class

AV = AutoViz_Class()

Visualization of Titanic dataset

Shape of your Data Set: (891, 12)
############## C L A S S I F Y I N G V A R I A B L E S ####################
Classifying variables in data set...
Number of Numeric Columns =…

Amazon vs Microsoft vs Google
The big three Amazon, Microsoft and Google provide important technologies and services in the field of cloud, ML and AI. Machine Learning as a service is being used by many corporate for delivering their deliverable to clients and also enhancing the solution on the go and with cloud and pandemic around the corner the importance of these service has increased many folds.

MLaas is provide in the cloud-based service for AI solutions. So here is a comparison of the big three below:

Usage wise there is been a study showing the comparing the three where…


Data can be best explained via visualization but can be understood or even remember based on concept/Domain knowledge. However the application of statistics, machine learning and deep learning comes with bucketful of hypothesis, back end algorithms, complex parameters and many postulations.

So to remember all sometimes becomes a burden but human understanding mostly long and short term memory. Long term memory is based on images or glimpse of the past.

Also, human is affected by a selective attention. We see what we want to see sometimes we ignore the behind stories.


Banks are key economy driver with respect to Country like India. Market fluctuates, peoples sentiments change and liquidity is impacted when RBI announces any results.

So as to have a healthy economy Risk with respect to finance and banking should be kept in check. Still many risk parameters are used in banking still there is a leakage which impacts in the form NPAs, Fraud, Money Laundering and Fund Diversion.

So what is Financial Risk in Banking sector:


The Question of What and why Clustering via Application of Clustering

A cluster can be defined as a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters.

Why Clustering?

  • To group similar objects / data points
  • To find homogeneous sets of customers
  • To segment the data in similar groups

Applications:

  • Marketing : Customer Segmentation & Profiling
  • Libraries : Book classification
  • Retail : Store Categorization
  • More Examples

Given the current predicament, as a data scientist, one has to understand the underlying approach that mobile companies take and how best can data science aid the business owners. So below is the key elements that makes Sales dependent on the data analytics :

  1. Instead of Advertising, they rely on
  • Product Placement ( i.e. with Celebrity and/ or popular shows)
  • Positive rave reviews

2. Focus of Unique Value proposition(UVP) — Beautiful design that works right out of the boss with ever smaller packaging

  • Focus on entire product instead of the a stand-out feature
  • Top of the line features and specification


Logistic regression is generalized form of linear regression. It helps in predicting and classifying the data as win or loss, true or False, Hit or flop or binary form as 0 or 1. It’s a Machine learning technique for binary classification.

The Logit function, also called Sigmoid function it’s an S-shaped curve that can take any real-valued number and map it into a value between 0 and 1, but never exactly at those limits.

1 / (1 + e^-z)

Where z= b0 + b1*x1 + b2*x2….. (linear equation where b1 , b2, are coefficients)


Elementary parallelism between Decision Tree and Random Forest

A simple example in the banking sector are the credit disbursal, which has a high risk in the current as there is a rise of NPAs. But bank has to to the business and for a customer to get a loan bank takes decision.

If bank takes Decision is based on a single variable like credit or CIBIL score, if the score is high approve the loan if less reject the proposal. So this forms a simple example of decision tree.

If bank takes multiple variable into consideration and multiple aspects of…


Tree are envisage naturally as having many branches and many leaves. Analogous to natural trees there is Decision Trees in Machine Learning, have branches, sub trees, nodes and leaves.

Jai Kushwaha

I am a 9yrs+ experienced Senior Consultant in Analytics and Model development with domain expertise in BFSI.

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