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# Supervised

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### Simple Guide to Logistic Regression in R and Python

Overview Get an introduction to logistic regression using R and Python Logistic Regression is a popular classification algorithm used to predict a binary outcome …

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### 6 Practices to enhance the performance of a Text Classification Model

Introduction A few months back, I was working on creating a sentiment classifier for Twitter data. After trying the common approaches, I was still …

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### Powerful ‘Trick’ to choose right models in Ensemble Learning

Introduction I hope you’ve followed my previous articles on ensemble modeling. In this article, I’ll share a crucial trick helpful to build models using ensemble …

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### Quick Guide to learn Statistics for R Users (with Titanic Data Set)

Introduction People are keen to pursue their career as a data scientist. And why shouldn’t they be? After all, this comes with a pride of …

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### Understanding basics of Recommendation Engines (with case study)

Introduction Ever wondered, “what algorithm google uses to maximize its target ads revenue?”. What about the e-commerce websites which advocates you through options such as ‘people who …

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### Building a Logistic Regression model from scratch

Do you understand how does logistic regression work? If your answer is yes, I have a challenge for you to solve. Here is an …

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### Build a Predictive Model in 10 Minutes (using Python)

Introduction I came across this strategic virtue from Sun Tzu recently: What has this to do with a data science blog? This is the …

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### Perfect way to build a Predictive Model in less than 10 minutes

Overview Hackathons involve building predictive models in a short time span The Data Preprocessing step takes up the most share while building a model …

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### Learn Gradient Boosting Algorithm for better predictions (with codes in R)

Introduction The accuracy of a predictive model can be boosted in two ways: Either by embracing feature engineering or by applying boosting algorithms straight …

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### Learn to use Forward Selection Techniques for Ensemble Modeling

Introduction Ensemble methods have the ability to provide much needed robustness and accuracy to both supervised and unsupervised problems. Machine learning is going to evolve …