CategoriesRegression Models Tags Linear Mixed Model Linear Regression R Programming One of the main limitations of regression analysis is when one needs to examine changes in data across several categories. This problem can be resolved by using a multilevel model, i.e. one that varies at more than one level and allows for variation between different groups or categories. This dataset from data.ok.gov contains information...
CategoriesAdvanced Modeling Tags K Means R Programming Unsupervised Learning Visualization has become a key application of data science in the telecommunications industry. Specifically, telecommunication analysis is highly dependent on the use of geospatial data. This is because telecommunication networks in themselves are geographically dispersed, and analysis of such dispersions can yield valuable insights regarding network structures, consumer demand, and availability. Data To illustrate this...
CategoriesAdvanced Modeling Tags R Programming Time Series When it comes to time series forecasts, conventional models such as ARIMA are often a popular option. While these models can prove to have high degrees of accuracy, they have one major shortcoming – they do not typically account for “shocks”, or sudden changes in a time series. Let’s see how we can potentially alleviate Related...
CategoriesVisualizing Data Tags Data Visualisation Principal Component Analysis R Programming Tips & Tricks The PCA (also known as Principal Component Analysis) is quite a handy tool for solving unsupervised learning problems. In other words, PCA can allow us to group unsupervised data into meaningful clusters, and visualize this in a way that allows us to make sense of our data. Let’s see how PCA can...
CategoriesAdvanced Modeling Tags Data Visualisation Neural Networks Prediction R Programming A neural network is a computational system that creates predictions based on existing data. Let us train and test a neural network using the neuralnet library in R. How To Construct A Neural Network? A neural network consists of: Input layers: Layers that take inputs based on existing data Hidden layers: Layers that use backpropagation...
Working with panel data in R: Fixed vs. Random Effects CategoriesAdvanced Modeling Tags Linear Regression Logistic Regression R Programming Video Tutorials Panel data, along with cross-sectional and time series data, are the main data types that we encounter when working with regression analysis. Types of data Cross-Sectional: Data collected at one particular point in time Time Series: Data collected across several time periods Panel Data: A...
