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Rohit Walimbe
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Nitin Pasumarthy commented on Rohit Walimbe's blog post Building machine learning models in Apache Spark using SCALA in 6 steps
"Nice post Rohit! Learned a lot. How to identify the hyperparameters (e.g. numTrees) of the bestModel above? How to perform a random search instead of grid search for hyperparameters?"
Apr 27
Nitin Pasumarthy liked Rohit Walimbe's blog post Building machine learning models in Apache Spark using SCALA in 6 steps
Apr 26
Haruna Isah liked Rohit Walimbe's blog post Building machine learning models in Apache Spark using SCALA in 6 steps
Apr 25
Leyli Garryyeva liked Rohit Walimbe's blog post Building machine learning models in Apache Spark using SCALA in 6 steps
Apr 25
Rohit Walimbe posted a blog post

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

Introduction:When dealing with building machine learning models, Data scientists spend most of the time on 2 main tasks when building machine learning modelsPre-processing and CleaningThe 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 model.IterationsThe optimization algorithms and finalizing the model accesses the data over…See More
Apr 25
Rohit Walimbe and serhat simsek are now friends
Apr 21
George Joseph liked Rohit Walimbe's blog post Handling imbalanced dataset in supervised learning using family of SMOTE algorithm.
Dec 26, 2018
Prasanth liked Rohit Walimbe's blog post Is it ‘always’ necessary to treat outliers in a machine learning model?
Nov 2, 2018
Haizea Rumayor Lazkano commented on Rohit Walimbe's blog post Overview and Classification of Machine Learning Problems
"Thank you Rohit, very interesting.With your permission I have reordered and completed with some new links. It seems to me that in this way is better understood.Regards,Overview%20and%20Classification%20of%20Machine%20Learning%20Problems.xlsx"
Aug 21, 2018
Katerina St liked Rohit Walimbe's blog post Overview and Classification of Machine Learning Problems
Aug 6, 2018
Karthik Dulam liked Rohit Walimbe's blog post Handling imbalanced dataset in supervised learning using family of SMOTE algorithm.
Apr 12, 2018
Rohit Walimbe posted a blog post

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

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, wrong data collection or wrong data entry. While…See More
Apr 11, 2018
Rohit Walimbe's blog post was featured

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

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, wrong data collection or wrong data entry. While…See More
Apr 11, 2018
ANISH XAVIER liked Rohit Walimbe's blog post Handling imbalanced dataset in supervised learning using family of SMOTE algorithm.
Dec 12, 2017

Profile Information

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
My Web Site Or LinkedIn Profile
http://www.linkedin.com/in/rohit-walimbe-36309b15
Professional Status
Manager
Years of Experience:
6
Your Company:
Tata Consultancy Services
Industry:
IT /Consultancy
Your Job Title:
Assistant Manager
Interests:
Finding a new position, Networking

Rohit Walimbe's Blog

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

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

Introduction:

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…

Continue

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

Posted on April 9, 2018 at 2:30am 0 Comments

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,…

Continue

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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Avoiding Look Ahead Bias in Time Series Modelling

Posted on April 21, 2017 at 6:00am 0 Comments

Any time series classification or regression forecasting involves the Y prediction at 't+n' given the X and Y information available till time T. Obviously no data scientist or statistician can deploy the system without back testing and validating the performance of model in history. Using the future actual information in training data which could be termed as "Look Ahead Bias" is probably the gravest mistake a data scientist can make. Even the sentence “we cannot make use future…

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