Final Project - Technology bibliographies - in Harvard style

In-text: (Alpaydin, 2020)

Your Bibliography: Alpaydin, E., 2020. Introduction to machine learning. London, England: The Mit Press, pp.235-236.

In-text: (Austin and Skidmore, 2021)

Your Bibliography: Austin, M. and Skidmore, F., 2021. Mortgage Lending Discrimination: A Review of Existing Evidence. [ebook] The Urban Institute. Available at: <https://www.urban.org/sites/default/files/publication/66151/309090-Mortgage-Lending-Discrimination.PDF> [Accessed 2 December 2021].

In-text: (Boehmke and Greenwell, 2020)

Your Bibliography: Boehmke, B. and Greenwell, B., 2020. Hands-on machine learning with R.

In-text: (A Laboratory for Recursive Partytioning [R package party version 1.3-9], 2021)

Your Bibliography: Cran.r-project.org. 2021. A Laboratory for Recursive Partytioning [R package party version 1.3-9]. [online] Available at: <https://cran.r-project.org/web/packages/party/index.html> [Accessed 15 December 2021].

In-text: (Dosalwar, Kinkar, Sannat and Pise, 2021)

Your Bibliography: Dosalwar, S., Kinkar, K., Sannat, R. and Pise, N., 2021. Analysis of Loan Availability using Machine Learning Techniques. [ebook] Available at: <https://www.researchgate.net/publication/354367264_Analysis_of_Loan_Availability_using_Machine_Learning_Techniques> [Accessed 6 December 2021].

In-text: (Gautam, Singh, Tyagi and Kumar, 2020)

Your Bibliography: Gautam, K., Singh, A., Tyagi, K. and Kumar, S., 2020. Loan Prediction using Decision Tree and Random Forest. [online] Irjet.net. Available at: <https://www.irjet.net/archives/V7/i8/IRJET-V7I8145.pdf> [Accessed 4 December 2021].

In-text: (Loan Data, 2021)

Your Bibliography: Kaggle.com. 2021. Loan Data. [online] Available at: <https://www.kaggle.com/zhijinzhai/loandata> [Accessed 6 December 2021].

In-text: (Madaan et al., 2020)

Your Bibliography: Madaan, M., Kumar, A., Keshri, C., Jain, R. and Nagrath, P., 2020. Loan default prediction using decision trees and random forest: A comparative study. [online] Iopscience.iop.org. Available at: <https://iopscience.iop.org/article/10.1088/1757-899X/1022/1/012042/pdf> [Accessed 4 December 2021].

In-text: (Digilytics AI on Role of Artificial Intelligence in SME loan origination, 2021)

Your Bibliography: Mortgage Finance Gazette. 2021. Digilytics AI on Role of Artificial Intelligence in SME loan origination. [online] Available at: <https://www.mortgagefinancegazette.com/fintech/digilytics-ai-role-artificial-intelligence-sme-loan-origination-22-07-2021/> [Accessed 4 December 2021].

In-text: (Mukid, Widiharih, Rusgiyono and Prahutama, 2018)

Your Bibliography: Mukid, M., Widiharih, T., Rusgiyono, A. and Prahutama, A., 2018. Credit scoring analysis using weighted k nearest neighbor. [online] Iopscience.iop.org. Available at: <https://iopscience.iop.org/article/10.1088/1742-6596/1025/1/012114/pdf> [Accessed 4 December 2021].

In-text: (What is the tidyverse?, 2021)

Your Bibliography: Rviews.rstudio.com. 2021. What is the tidyverse?. [online] Available at: <https://rviews.rstudio.com/2017/06/08/what-is-the-tidyverse/> [Accessed 6 December 2021].

In-text: (Rejection rates for SMEs business loans UK | Statista, 2021)

Your Bibliography: Statista. 2021. Rejection rates for SMEs business loans UK | Statista. [online] Available at: <https://www.statista.com/statistics/461659/smes-rejections-loans-united-kingdom/> [Accessed 2 December 2021].

In-text: (Vangaveeti et al., 2020)

Your Bibliography: Vangaveeti, S., Venna, N., Kidambi, P., Marneni, H. and Maganti, N., 2020. LOGISTIC REGRESSION BASED LOAN APPROVAL PREDICTION. [online] Jctjournal.com. Available at: <http://www.jctjournal.com/gallery/39-may-2020.pdf> [Accessed 4 December 2021].

In-text: (Wong and Yeh, 2019)

Your Bibliography: Wong, T. and Yeh, S., 2019. Weighted Random Forests for Evaluating Financial Credit Risk. [online] Core.ac.uk. Available at: <https://core.ac.uk/download/pdf/228834303.pdf> [Accessed 4 December 2021].

In-text: (Zhu et al., 2019)

Your Bibliography: Zhu, L., Qiu, D., Ergu, D., Ying, C. and Liu, K., 2019. A study on predicting loan default based on the random forest algorithm. [ebook] ScienceDirect. Available at: <https://reader.elsevier.com/reader/sd/pii/S1877050919320277?token=7735D0604FE7270F9E052AA2C1DF3597CAA20FD3E24EEF72A0A791A94748CDF738890D29FE47B7432F16161D52A0CCCD&originRegion=eu-west-1&originCreation=20211202061154> [Accessed 2 December 2021].