Jan 22 2015

Decision Tree Algorithms – Simplified

Information Gain, Decision Tree

In last article, we looked at the basics of Decision tree and how it helps in classifications. We also looked at advantages and disadvantages of using decision trees. One of the advantage of using Decision tree is that it efficiently identifies the most significant variable and splits the population on it. In previous article, we developed …

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Jan 20 2015

Model Performance metrics: How well does my model perform? – Part 2

curves

The popularity of the last article forces us to publish this article this soon. In the last article, we discussed a few performance metrics used for classification problems. We saw confusion matrix is most commonly used with class output models, however can also be used with probability output models using a threshold probability. We also …

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Jan 19 2015

QlikView learning path – the only resource you need to master QlikView

We launched our learning paths last week with Data Science in Python. The Python learning path received awesome response from not only our audience, but data science community world wide. The learning path received more views in a day than what some of our best written articles get in a month! Today, we launch the …

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Jan 15 2015

Decision Tree – Simplified!

Decision Tree, Machine Learning, Python, Orange, Kaggle

I started working as a business analyst in my previous organisation. I transitioned from a Business Intelligence (BI) Analyst to become a Business Analyst. During the initial days of tenure as a business analyst, I had a bias towards using a classification technique – DECISION TREE. This was because of its inherent simplicity and many advantages. We …

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Jan 13 2015

Launch of learning path – Data Science in Python

We are jumping on our feets right now! We can’t find any other way to express our excitement. We said that 2015 is going to be a year when Analytics Vidhya will become the place to learn analytics and data science. We launched our discussion forums 2 weeks back and there are awesome discussions already …

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Jan 11 2015

Model performance metrics: How well does my model perform? – Part 1

Liftdecile

In case you are preparing for an analytics interview, you have hit a jackpot. This blog will give you answers to at least 2 – 3 questions, which are likely to be asked in the interview. In case you already know some of the metrics discussed in this article, it will still be worth reading to brush …

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Jan 09 2015

Comprehensive Introduction to merging in SAS

SAS, Merge

In my previous article, “Combining data sets in SAS – Simplified“, we discussed three methods to combine data sets – appending, concatenating and Interleaving. In this article, we will look at the most common and frequently used method of combining data sets – MERGING or JOINING. The need for joining / merging datasets: Before jumping …

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Jan 06 2015

Image processing and feature extraction using Python

7

No doubt, the above picture looks like one of the in-built desktop backgrounds. All credits to my sister, who clicks weird things which somehow become really tempting to eyes. However, we have been born in an era of digital photography, we rarely wonder how are these pictures stored in memory or how are the various transformations made in …

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Jan 05 2015

Scikit-learn in Python – the most important Machine Learning tool I learnt last year!

Scikit-learn Logo

This article went through a series of changes! I was initially writing on a different topic (related to analytics). I had almost finished writing it. I had put in about 2 hours and written an average article. If I had made it live, it would have done OK! But something in me stopped me from making …

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Jan 01 2015

Welcome 2015 with new, better and more helpful Analytics Vidhya

Analytics Vidhya Logo

Over the last 12 months, we went from a small, little known blog on analytics to one of the most engaging and helpful community in Data Science across the globe. If 2014 was big (for us), the plans for 2015 are grand! Before I pull out any rabbits from my hat, I want to thank our …

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