Business & Management

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    Machine learning based prediction model for building energy ratings in Ireland
    (Dublin Business School, 2024-05) Amin, Basma; Kshirsagar, Vivek
    Energy can be defined as the driving force behind our modern society. Building energy consumption has emerged as one of the prime contributors to the total energy consumption, due to urbanisation and a colossal increase in the world population. It has been reported that buildings consume 40% of the global energy consumption, and release 38% of Carbon dioxide (CO2). Governments and policymakers are on the lookout for uncovering advanced methods to prepare nations to control climate change and move towards a more sustainable world [1]. Building Energy Ratings have been in the limelight in this regard, as cities have basically turned into blocks of commercial and residential buildings that require energy to fully function. Researchers have proposed the active use of machine learning and technology to support in the agenda. This study has used an official dataset for Irish Building Energy Ratings and emphasises on commissioning the advantages of machine learning to predict energy ratings in Ireland. This study has applied multiclass classification using Logistic Regression, Random Forest Classifier, XGBoost (XGB), Support Vector Classifier and K Nearest Neighbour to train the model to accurately predict building energy ratings. [2]. After the model was applied, it was observed that the Random Forest Classifier and XGBoost were the most efficient models for the purpose of this study. Results from this study can lay a foundation for future studies in the field on building energy ratings in residential and non-residential areas. Energy upgrades have become increasingly common in Ireland and those effected by a better energy certificate have been keen on delving deeper into the possibilities that the Government has offered in this regard. Incentives and grants are offered to homeowners and landlords for working towards improving the energy ratings of their respective dwellings. The machine learning model implemented in this study can help individuals gauge the energy ratings of their buildings by plugging in the details and features. It would also give individuals a chance to contribute towards sustainability and efficiently utilising scare energy resources of the planet. This would also save time in assessing the Building Energy Ratings (BER) as it is a detailed process and has a number of formalities before a final rating is reached at
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    Impact of funding and geographical factors on software startups' success
    (Dublin Business School, 2024-05) Ondiba, Florence; Prakash, Satya
    The purpose of this study was to investigate and adress two important questions regarding the success of software startups. To begin, it investigated the level of impact that different types of funding structures and geographical factors have on the success of the software startup companies. It explored various machine learning models to predict the outcomes of startup ventures, taking into account important features, model performance and cost-effectiveness. As demonstrated in this report, the research provided answers to these questions. The study identified the primary factors that contribute to over 64 per cent of the success or failure of a software startup company. Location-region accounts for 18%, Time initial funding has received accounts for 16%, timing of Final Funding accounts for 15%, Access to Venture Capital accounts for 11%, and Location - Country accounts for 5%. Logistic regression emerged as the most suitable model for deployment, achieving an accuracy of 96.58%, precision of 96.51%, recall of 100%, and an F1 score of 98.22%. This is by utilising the CRISP-DM methodology, Python code, and Power BI for data scrutiny and analysis. In addition, this model provides significant cost savings, which amount to 73.81 million dollars. The study does, however, acknowledge that there are challenges associated with limitations in the dataset scope and timeframe. These findings highlight the significance of conducting a comprehensive analysis of a startup, which should include aspects such as financial evaluation, geographical analysis, and predictive modelling.
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    Comparing machine learning algorithm for predicting loan application for performance enahncement
    (Dublin Business School, 2024-05) Oparaocha, Elizabeth; Nwankire, Charles
    This study evaluates various machine learning algorithms for predicting bank loan status using the CRISP-DM methodology. The research utilizes a dataset from Kaggle, focusing on algorithms such as Decision Trees, Support Vector Machines, AdaBoost, Random Forests, K-Nearest Neighbours, and Logistic Regression. Models were optimized using GridSearchCV, with recall as the primary metric to highlight the importance of accurately detecting loan repayment patterns. The findings demonstrate that the Support Vector Machine model was the most effective, achieving a recall score of 0.99and an F1 score of 0.96. Ensemble methods, which combine multiple models, notably improved prediction accuracy while maintaining interpretability. This study identifies the most effective algorithms and provides insights into factors influencing loan decisions, offering practical recommendations for banks to reduce bad loan rates and promote sustainable lending practices through advanced machine learning techniques.
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    Can Retaining Older Workers in the Workplace Benefit Organisations in South Africa?
    (Dublin Business School, 2024) Schroeder, Julinda; Browne, Andrew
    The aim of the study was to explore whether retaining older workers in the workplace could benefit organisations in South Africa, against the backdrop of increasing numbers of people around the world being forced to work longer, or return to the workforce after retirement. This was due to socio-economic factors, which were currently being further exacerbated by the global inflationary impact on pensions and savings. Apprehension about losing meaning and purpose in life after retirement were further factors that emerged during the research for this dissertation. The objectives were thus to explore the transitioning of older, senior employees in an organisation to a coaching and mentoring role to potentially help mitigate the serous skills shortage in South Africa. Data collection took the form of seven semi-structured, online interviews with the identified participants in South Africa. The data was analysed using an inductive thematic analysis to describe and interpret the information. The interviewees confirmed that older members of the workforce proved to be a rich source of talent and experience for the organisations that engage them and have, in many instances, become catalysts for change and intergenerational diversity in the workplace. Despite the positive potential of retaining more mature members of the workforce, however, the reality of the current socio-economic situation in South Africa, proved to be far more complex than was anticipated at the outset of the study. To this end, the objectives of this study had to be revisited and some harsh truths had to be acknowledged. Thus, it was found that transitioning more mature members of the workforce to a coaching and mentoring role would require a significant mindset shift and more structured human resource management guidelines and processes to facilitate it.
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    Perform the Stock Prediction Using the Sentiment Analysis and Time Series Forecasting Approaches to Determine the Optimal One
    (Dublin Business School, 2024) Thimmaiah, Kavya; Laiho, Heikki
    Stock returns are affected by a variety of factors, among which the social media remarks of public figures are one of the more important aspects on the stock market trend. On top of that, latest news about the product of the stock also matters. In this paper, we determine the sentiment type of public figures' social media remarks from the perspective of textual sentiment, and compare them with the stock chart of the day to analyse the connection between the two. Specifically, we first construct a dataset of public figures' social remarks and classify the sentiment types, and then we use the network model BERT for training to be able to judge the sentiment type of a new remark when it is inputted, which serves as a basis for stock prediction. The experiment shows that the public figure's speech and the news will have a strong impact on the stock trading on the same day, but the impact is small for a long time, at the same time, the more influential the public figure is, the more obvious the impact on the stock. The development and wealth of countries depend heavily on the stock market. Data mining and artificial intelligence methods are required to analyse stock market data. The financial success of particular businesses is one of the important factors that has a significant impact on stock price volatility. However, news reports also have a significant impact on how the stock market moves. In this research, we use sentiment classification to use non-measurable data, such as financial news articles, to forecast a company's future stock trend. We seek to cast light on the effect of news reports on the stock market by analysing the connection between news and stock movement. Our study seeks to advance knowledge of the function of news sentiment in forecasting stock market trends. The dataset used in this study consists of news headlines from the financial news website, Financial Times, and the prediction task is to classify the direction of the stock price changes as either positive or negative. The purpose of this study is to evaluate the effectiveness of sentiment analysis for stock prediction and to compare the performance of different algorithms.