Machine Learning Predictive analytics Prescriptive analytics

Machine Learning Predictive analytics Prescriptive analytics

This assignment involves machine learning. Write in details about following with examples: Descriptive analytics, Predictive analytics, and Prescriptive analytics.

Machine Learning-Write in details about following with examples

Machine Learning
1.            Write in details about following with examples:
Firstly,  Descriptive analytics
Secondly, Predictive analytics

Thirdly, Prescriptive analytics

2.            Write theory and mathematics of following algorithms:

Firstly, Multivariate Feature Selection

Secondly, Random forests

Thirdly, Neural Network

Fourthly, Support vector machines

Fifthly, 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 uni-variate 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). Comment on results.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.