Rohit Walimbe
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Short Bio:
Experienced Data Scientist and Quant with a demonstrated history of working in various domains like BFSI, Manufacturing, Retail, Risk, etc. Strong knowledge of Machine Learning, Predictive Analytics, Network Theory, Time Series Analysis, Trading Systems, Derivative Pricing and Financial Mathematics. Skilled in R, Python, Matlab, VBA and SQL
Tata Consultancy Services
Job Title:
Assistant Manager
IT /Consultancy
LinkedIn Profile:
Finding a new position, Networking

Rohit Walimbe's Blog

Hiring the right data scientist for the organisation

Posted on June 9, 2019 at 6:03am 0 Comments

Any organisation needs talented, hardworking and skilled employees irrespective of department, business unit or a team. But finding and nurturing such talent can be challenging sometimes. When it comes to data science field, with rapid change and demand in the technology, many organisations have set up the data science teams. A successful data science team has 3 major strengths, A-availability of data, B- infrastructure and most importantly C - the “right” data scientists. 



Building machine learning models in Apache Spark using SCALA in 6 steps

Posted on April 21, 2019 at 9:00pm 1 Comment


When dealing with building machine learning models, Data scientists spend most of the time on 2 main tasks when building machine learning models

Pre-processing and Cleaning

The major portion of time goes in to collecting, understanding, and analysing, cleaning the data and then building features. All the above steps mentioned are very important and critical to build successful machine learning…


Is it ‘always’ necessary to treat outliers in a machine learning model?

Posted on April 9, 2018 at 2:30am 1 Comment

Outliers is one of those issues we come across almost every day in a machine learning modelling. Wikipedia defines outliers as “an observation point that is distant from other observations.” That means, some minority cases in the data set are different from the majority of the data. I would like to classify outlier data in to two main categories: Non-Natural and Natural.

The non-natural outliers are those which are caused by measurement errors,…


Handling imbalanced dataset in supervised learning using family of SMOTE algorithm.

Posted on April 24, 2017 at 10:00pm 0 Comments

Consider a problem where you are working on a machine learning classification problem. You get an accuracy of 98% and you are very happy. But that happiness doesn’t last long when you look at the confusion matrix and realize that majority class is 98% of the total data and all examples are classified as majority class. Welcome to the real world of imbalanced data sets!!…


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