Machine Learning

Machine Learning

1. Write in details about following with examples:
   • Descriptive analytics
   • Predictive analytics
   • Prescriptive analytics
2. Write theory and mathematics of the following algorithms:
   • Multivariate Feature Selection
   • Random forests
   • Neural Network
   • Support vector machines
   • Mathematical modeling, 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.
2. Perform bi-variate data exploration and comment on results.
3. Which attributes predict churn behavior? Discuss finding.
   Predictive analytics
4. Perform multivariate feature selection and comment on results
5. Build predictive models using Random Forests, Neural Network, and Support Vector Machines. Discuss the performance of the models and compare the models.

Prescriptive analytics

1. Run mathematical modeling, simulation and optimization techniques to optimize customer retention (or avoid churn). Comment on results.

Sample Solution