R

Machine Learning

Introduction to Data Imputation | What to do when data is missing? (Part I)

Yes! You've got the coolest dataset on your hard drive. Countless hours of fun are waiting for you. Except, some rows have missing values and your model might not be happy with those. But you have the perfect solution! You can just ignore them (nobody said delete them, right?)! Now, why this might not be the best idea? Let's dig deeper into data imputation using R.

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Statistics

Predicting with Small Data using Bayes

We will use a small dataset of just 74 rows to create a Bayesian model (Multinomial Logistic Regression) using JAGS. Do you think you can make good predictions on such small dataset?

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Data Science

Making Twitter Cool Again

We will assign tweets, fetched using the Twitter API, to topics/categories without training data. Using a Latent Dirichlet Allocation (LDA) topic model and R!

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Data Science

Predicting House Prices

We will try to predict the sale price of houses in King County, USA using a decision tree model. Later, we will improve our predictions using Gradient Boosted trees. Using R!

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