MIS10060 Introduction to Bus Analytics UCD Assignment Example

Bus analytics is a field of data analytics that deals with the collection, analysis, and interpretation of data related to public transportation systems. It can be used to improve the efficiency and performance of bus systems by identifying areas where improvements can be made.

The main components of bus analytics are passenger flow analysis, route optimization, and schedule planning. Passenger flow analysis involves tracking the movement of passengers through a transit system to identify bottlenecks and chokepoints. Route optimization involves analyzing data on passenger demand and travel patterns to create more efficient routes.

Schedule planning involves creating schedules that optimize passenger wait times and travel distances. Data collection can be done in various ways: by collecting ridership data from Statistics Canada, accessing transit availability and fares databases, and tracking the location of buses at regional or municipal levels.

Assignment Activity 1: Identify the main classes of analytics problems arising in business and industry

The three main classes of analytics problems that arise in business and industry are descriptive analytics, predictive analytics, and prescriptive analytics.

Descriptive analytics is the process of describing patterns and trends in data to better understand what has happened in the past.

Predictive analytics is the process of using past data to predict future outcomes.

Prescriptive analytics is the process of using past data and predictive models to recommend specific actions that should be taken to achieve the desired outcome.

Assignment Activity 2: Formulate, explain, and distinguish between various forms of regression, classification, and linear programming models.

There are a variety of regression models, but they all attempt to explain or predict one variable (the outcome) using one or more predictor variables. The goal is to find the best-fitting line or curve through the data to minimize the squared distance between the observed values and the fitted values.

There are several different types of regression models, including linear regression, logistic regression, and polynomial regression. Linear regression is used when the predictor variable is continuous (like height or weight), while logistic regression is used when the predictor variable is categorical (like gender). Polynomial regression can be used when the relationship between the predictor and outcome variables is nonlinear.

Each type of regression model has its formulae and other techniques that are used from time to time. There are R packages available for both regression and classification problems, but individuals should also know how to solve them individually.

Assignment Activity 3: Create and implement regression, classification, and linear programming models (in suitable software).

Creating and implementing regression, classification, and linear programming models can be a complex process. However, with the right tools and resources, it can be a manageable task.

One of the most important things to consider when creating or implementing these models is choosing the right data set to work with. The data set should be representative of the population you’re trying to model, and it should also contain all of the necessary information for predicting outcomes.

Once you have your data set selected, you can start modelling! There are several different modelling techniques that you can use, depending on the type of problem you’re trying to solve. Linear programming is often used for optimizing business processes, while regression analysis can be used to predict future outcomes. To choose between these two models, you’ll need to understand some basic concepts first. System choice must also be identified and studied to find the best results. Depending on which technique you’re using, remember that there are also smaller differences in the choices for data input, output, and parameter values. This means the choice must be made more rigorously in some cases than others.

Assignment Activity 4: Test the models and interpret results in a form suitable for a business client or manager.

To effectively test the models and interpret the results for a business client, it is important to first understand what the client is looking for. Sometimes, clients will have specific questions that need to be answered to make informed business decisions. In other cases, clients may simply want a general overview of how the models work and what they can predict.

It is also important to be aware of any limitations of the models. For example, if a model is based on historical data, it may not be accurate enough to predict future trends. In such cases, it is important to carefully examine all of the available data and then form a judgment about what is likely to happen in the future.

 Ultimately, it is important to work with a client to aid them in making their environment more prosperous through predictive analytics. This can be done by providing them with enough data, allowing them to test the models, and helping them address any modifications they might desire.  Although how predictive analytics is used can vary greatly depending on business practices, it is an important concept that must be understood no matter what kind of analytics you’re using.

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