Theory of Machine Learning

Machine Learning

1. 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

• 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.

2. Perform bi-variate data exploration and comment on results.

3. Which attributes predict churn behavior? Discuss on 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 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). C

omment on results.