Rohit Garg
Rohit Garg
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Rohit Garg has close to 7 years of work experience in field of data analytics and machine learning. He has worked extensively in the areas of predictive modeling, time series analysis and segmentation techniques. Rohit holds BE from BITS Pilani and PGDM from IIM Raipur.

60 Interview Questions On Machine Learning

We frequently come out with resources for aspirants and job seekers in data science to help them make a career in this vibrant field. Cracking interviews especially where understating of machine learning is needed can be tricky. Here are 60…

40 Interview Questions On Statistics For Data Scientists

We frequently come out with resources for aspirants and job seekers in data science to help them make a career in this vibrant field. Cracking interviews especially where understating of statistics is needed can be tricky. Here are 40 most…

A Beginners Guide To Regression Techniques

Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent (target) and independent variable (s) (predictor). This technique is used for finding the relationship between the variables. Example: Let’s say, you want to estimate growth in sales…

A Step-by-Step Guide To Creating Credit Scoring Model From Scratch

A credit scoring model is a statistical tool widely used by lenders to assess the creditworthiness of their potential and existing customers. The basic idea behind this model is that various demographic attributes and past repayment behavior of an individual…

A Primer To Monte Carlo Simulation in Python

Simulation is acting out or mimicking an actual or probable real life condition, event, or situation to find a cause of a past occurrence (such as an accident), or to forecast future effects (outcomes) of assumed circumstances or factors. Whereas…

A Primer to Ensemble Learning – Bagging and Boosting  

Ensemble is a machine learning concept in which multiple models are trained using the same learning algorithm. Bagging is a way to decrease the variance in the prediction by generating additional data for training from dataset using combinations with repetitions…

Banking Analytics Basics – Developing A Customer Level Behaviour Scorecard

We have various types of scorecards like acquisition, behaviour, income, collection etc. The purpose of building a behaviour scorecard is to monitor the performance of booked accounts, i.e. accounts which are already in Bank’s books. We use the scorecard to…

7 Types of Classification Algorithms

The purpose of this research is to put together the 7 most common types of classification algorithms along with the python code: Logistic Regression, Naïve Bayes, Stochastic Gradient Descent, K-Nearest Neighbours, Decision Tree, Random Forest, and Support Vector Machine 1…

Computing Roll Rates using Markov Chain

Roll rate is the percentage of customers who become increasingly delinquent on their account. Banks use roll rates to predict credit losses based on delinquency. Analysing roll rates is an effective way to review overall trends and estimate future performance.…

Time Series Modelling and Stress Testing – Using ARIMAX

The primary objective of CCAR secured model is to stress test the business unit’s mortgage balances using a set of scenarios provided by the Federal Reserve Bank (FRB) as well as Bank Holding Company’s (BHC). Therefore, a critical step in…

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