Regression and Linear Programming Models

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Predictive Analytics Task

To help a company improve their brand recognition and optimise the impact of their social media posts, the company would like to know which characteristics of the post impacts the social media activity the most (See Excel Dataset: Social Media Metrics).

a) Develop a linear regression model in R for each of the social media metric of your choice (minimum 3).

b) Identify the post characteristics that are relevant in predicting the performance of the social media metrics

c) Provide further analysis to show how each relevant feature influenced the performance of the social media metrics

2. Prescriptive Analytics Task

To attract more customers, the company had identified four advertisement media to advertise their products (See Excel Dataset: Advertising Media). To ensure a balanced use of advertisement media, TV advertisement must not exceed 50% of the total number of advertisements, flyer and radio must not exceed 10% of the total number of advertisements. In addition, there should be at least 10 online advertisements. The advertising budget is £190,000.

a) Apply prescriptive analytics to recommend the best possible option. State any assumptions that you have made.

b) Develop and solve a linear optimisation model in Excel to determine the number of each type of media to use such that the number of audiences reached is maximised.

c) Perform what-if analysis for the optimisation model.

d) Explain the best option and trade-offs that must be made to increase the numbers of audience reached. Use the Sensitivity Report to facilitate your analysis.

Sample Solution

problem in face recognition if there were no witnesses [12]. But if there were witnesses, they can match the face with a sketch. And that process can also be reversed.

iv. Face recognition in sketches
When a face cannot be obtained from a picture or from security images, a forensic sketch can be a good substitute based on the description a witness is giving. [23] These sketches include facial features and depending on those facial features, the forensic sketch can correspond to a mugshot in the police’s database full of mugshots. To see if a face matches with a mugshot from the database, face recognition is used. Again, the efficiency of this method depends on how good the quality of the sketch is. In the database there are many mugshots, so to increase the chance that the sketch will match with the good mugshot, a law enforcement agency will have to look at the best-retrieved results. They can look for example at the best 30 or 50. If the police force will only look at the best result, then the chance to find the mugshot that they want will be smaller than if they look at the best 30 or 50 results. Beside the quality, the computer can make mistakes and that is also a reason why looking at the best 30 or 50 is better than just looking at the best result of the computer [20].

4. Dangers of using facial recognition techniques
Using facial recognition in the real world is becoming more normal, and the technologies are improving very rapidly. It is just a matter of time until facial recognition is something people will use in their daily life. Until this is the case, there are still a lot of things to improve and the dangers that this technique bring need to be considered, and solutions need to be found. In this paper, a few of these dangers will be discussed and analyzed.
i. Crime opportunities will increase
When one is using facial recognition to prevent crime, it will also create opportunities for criminals. Stalking and identity fraud, for instance, will be made much easier. Cameras with facial recognition technology can be used to keep track of people. These cameras do not have to be expensive, which will make it more tempting for crim

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