Multivariate Feature Selection Support vector machines

Multivariate Feature Selection Support vector machines

This essay entails a paper on the Multivariate Feature Selection and the Support vector machines in making calculations for data typesetting. Multivariate Feature Selection occurs where there is a need to select the different types of problems to be solved by a specific item in mathematics.

Multivariate Feature Selection Support vector machines

Firstly, write in details about following with examples. Descriptive analytics. Predictive analytics. Prescriptive analytics 2. Write theory and mathematics of following algorithms. Multivariate Feature Selection. Random forests. Neural Network. Support vector machines

Secondly, mathematical modelling, simulation and optimization Note: Cite referenced materials. Use Dataset at : https://www.kaggle.com/jpacse/telecom-churn-new-cell2cell-dataset Note: use Python as language Descriptive analytics 1. Perform univariate data exploration and comment on results.

Also, perform bi-variate data exploration and comment on results. Which attributes predict churn behavior? Discuss on finding. Predictive analytics. Perform multivariate feature selection and comment on results. Build predictive models using Random Forests, Neural Network and Support Vector Machines.

Furthermore, the application of gene expression data to the diagnosis and classification of cancer has become a hot issue in the field of cancer classification.

Furthermore, Gene expression data usually contains a large number of tumor-free data and has the characteristics of high dimensions.

Consequtively, in order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new feature selection method, namely, support vector machine based on recursive feature elimination and parameter optimization.

Also, the grid search (GS) algorithm, the particle swarm optimization (PSO) algorithm, and the genetic algorithm (GA) are applied to search the optimal parameters in the feature selection process. The application of gene expression data to the diagnosis and classification of cancer has become a hot issue in the field of cancer classification. Gene expression data usually contains a large number of tumor-free data and has the characteristics of high dimensions.

Gene determinant

Thirdly, in order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new feature selection method, namely, support vector machine based on recursive feature elimination and parameter optimization (SVM-RFE-PO).

Lastly, discuss on performance of the models and compare the models. Prescriptive analytics 6. Run mathematical modelling, simulation and optimization technique to optimize customer retention (or avoid churn). Comment on results.