Predictive Analytics

Predictive methodologies use knowledge, usually extracted from historical data, to predict future, or otherwise unknown, events. Analytic techniques that fall into this category include a wide range of approaches to include parametric methods such as time series forecasting, linear regression, multilevel modeling, simulation methods such as discrete event simulation and agent-based modeling; classification methods such as logistic regression and decision trees; and artificial intelligence methods such as artificial neural networks and bayesian networks. The following tutorials walk you through common forms of predictive analytics.

Machine Learning

• Preparing for Machine Learning Tasks
• Linear Regression
• Linear Model Selection
• Naïve Bayes
• Logistic Regression
• Regularized Regression
• Multivariate Adaptive Regression Splines
• Regression Trees & Bagging
• Random Forests
• Gradient Boosting Machines
• Linear & Quadratic Discriminant Analysis
• Support Vector Machines

Deep Learning

• Neural Network Fundamentals
• Neural Network for Regression
• Neural Network for Classification
• Feedforward Deep Learning with Keras & Tensorflow

Time Series

• Exploring & Visualizing Times Series
• Benchmark Methods & Forecast Accuracy
• Moving Averages
• Exponential Smoothing

Machine Learning Interpretation

• Local Interpretable Model-agnostic Explanations (LIME)
• Model Interpretability with DALEX
• Interpreting Machine Learning Models with the iml Package

Feature Engineering

• Creating text features bag-of-words, n-grams, parts-of-speach and more

Resampling Methods

• Resampling Basics