### 1.2 Develop linear models using the lm function in R and the .ols function in Python.

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# Unit DS03: Fundamentals of Predictive Modelling

Level 7 Diploma in Data Science

NOTE: Kindly share your UPDATED exam paper to the with an updated Case Study (if any)

Unit code: R/618/4972
RQF Level: 7

Aim
This unit provides a strong foundation for predictive modelling. Its objective is to define the entire modelling process with the help of real life case studies. Many concepts in predictive modelling methods are common and therefore, these concepts will be discussed in detail in this unit. A good understanding of predictive modelling leads to a smart data scientist as many business problems are related to successfully predicting future outcomes.

Learning Outcomes and Assessment Criteria

 Learning Outcomes. When awarded credit for this unit, a learner will be able to: Assessment Criteria. Assessment of this learning outcome will require a learner to demonstrate that they can: 1. Carry out global and individual testing of parameters used in defining predictive models. 1.1 Evaluate dependent variables and predictors. 1.2 Develop linear models using the lm function in R and the .ols function in Python. 1.3 Interpret signs and values of estimated regression coefficients. 1.4 Interpret output of global testing using F distributions. 1.5 Identify significant and insignificant variables. 2. Validate assumptions in multiple linear regression. 2.1 Resolve multicollinearity problems. 2.2 Revise a model after resolving the problem. 2.3 Assess the performance of the ridge regression model. 2.4 Perform residual analysis – graphically & using statistical tests to analyse results. 2.5 Resolve problems of non-normality of errors and heteroscedasticity. 3. Validate models via data partitioning, out of sample testing and cross-validation. 3.1 Develop models and implement them on testing data in accordance with the specification. 3.2 Evaluate the stability of the models using k-fold cross validation. 3.3 Evaluate influential observations using Cook’s distance and hat matrix.

Assessment Guidance
To demonstrate all learning outcomes and assessment criteria, each unit should follow the same assessment methodology:

• Formative: Weekly assignments focussing on knowledge and understanding of technical skills using sample data sets over a period of 2 weeks and participation in weekly live classrooms and discussion groups;
• Summative: 1. Formal timed exam testing technical knowledge 2. Component of two individual course projects based on real word data analytics

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