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A comprehensive beginners guide for Linear, Ridge and Lasso Regression
Introduction I was talking to one of my friends who happens to be an operations manager at one of the Supermarket chains in India. …
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Cheatsheet: Scikit-Learn & Caret Package for Python & R respectively
Introduction For any Python or R practitioner, this article will prove to be a boon. We provide you cheatsheets for the most widely used machine …
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Introduction to Feature Selection methods with an example (or how to select the right variables?)
Introduction One of the best ways I use to learn machine learning, is by benchmarking myself against the best data scientists in competitions. It …
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18 Free Exploratory Data Analysis Tools For People who don’t code so well
Introduction Some of these tools are even better than programming (R, Python, SAS) tools. All of us are born with special talents. It’s just …
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Winners Talk: Top 3 Solutions of The Seer’s Accuracy Competition
Introduction Surprises arrive when you expect them least to arrive. The Seer’s Accuracy turned out to be a challenging surprise for data scientists. So, what changed …
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A Complete Tutorial to learn Data Science in R from Scratch
Introduction R is a powerful language used widely for data analysis and statistical computing. It was developed in early 90s. Since then, endless efforts …
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8 Ways to deal with Continuous Variables in Predictive Modeling
Introduction Let’s come straight to the point on this one – there are only 2 types of variables you see – Continuous and Discrete. …
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Simple Methods to deal with Categorical Variables in Predictive Modeling
Introduction Categorical variables are known to hide and mask lots of interesting information in a data set. It’s crucial to learn the methods of …
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Getting started with Machine Learning in MS Excel using XLMiner
Introduction Machine Learning is nothing but building a ‘machine’ which ‘learns’ from its experience. And, becomes better with experience – just like humans. We also …
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Simple Guide to Logistic Regression in R
Introduction Every machine learning algorithm works best under a given set of conditions. Making sure your algorithm fits the assumptions / requirements ensures superior …
