Model predictive control algorithms applied to various dynamic systems, developed in Python. Using the do-mpc and casadi package in Python. Systems implemented: Spaceship Kinematic bicycle model Contents models: Contains subfolders for each model. Model directory will have a description of the system,...
Code for the CUP Elements on text analysis in Python for social scientists analysistext-classificationclusteringtext-analysispredictionembeddingsneural-networkstopic-modelingdata-analysispredictive-modelingsocial-sciencesclassification-models UpdatedSep 11, 2022 ...
Overall, the book is a suitable reference book for data science practitioners to learn exploratory data analysis for predictive models and its applications using R or Python software.doi:10.1080/00224065.2021.1977101Bing SiJournal of Quality Technology...
Before, you may have had to integrate Tableau with R and Python in order to perform advanced statistical calculations and visualize them in Tableau. Now, you can select targets and predictors by updating the variables and visualizing multiple models with different combinations of predictors. The data...
Business users make use of the models they need for their work—within the same tool they are doing the rest of their analyses—live on the data that is relevant to them. This makes the exclusive Data Science domain of robust, real-time, model-scoring possible in a BI platform and avai...
We propose a simple framework—meta-matching—to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in small-scale studies. The key consideration is that a unique phenotype from a boutique study likely correlates with (but is not the same as) related...
This study examines CO2 emissions and vehicle energy consumption at high-traffic intersections in urban areas. Existing emission models at the macro, meso, and microscales often fail to accurately represent real traffic conditions, especially at intersections with frequent stop-and-go maneuvers. New p...
However, sometimes the questions we want to ask require us to reshape or transform the raw data we are given. This will happen frequently in later chapters, when we develop features for predictive models. Pandas provide many tools for performing this kind of transformation. For example, while ...
Case study: fitting classifier models in pyspark Summary Chapter 6. Words and Pixels – Working with Unstructured Data Working with textual data Principal component analysis Images Case Study: Training a Recommender System in PySpark Summary Chapter 7. Learning from the Bottom Up – Deep Networks and...
Wrangling data and bringing it in the form you desire is a big challenge before one proceeds to modelling. But, once done, it opens up a plethora of insights and information to be discovered using predictive models. As Bob Marley said, "If it is easy, it won't be amazing; if it is...