Assignment Details:-

• Number of Words: 1500 + Technical work
• Subject: Machine Learning

Bankruptcy Prediction Using Machine Learning

Bankruptcy prediction using machine learning. – working on Google Colaboratory

You will need to run machine learning for classification for the analysis.

The data were collected from the Taiwan Economic Journal for the years 1999 to 2009. Company bankruptcy was defined based on the business regulations of the Taiwan Stock Exchange.

Using appropriate machine learning algorithms and conventional statistical methods, write a report on the followings.

A. The estimators you use in the data analytics. This is the core of your discussion.

1. Which estimators you use for the analysis? E.g., neural network, logistic regression, k-nearest neighbour.
2. Explain why you choose them. What are their strengths and limitations?
3. How well each estimator performs such as their accuracy?
4. Discuss any limitations in the data, how these affect the estimators’ performance, and how you address them.

B. Extract insights from the data. Discuss the implications of your findings within a business decision context. Position yourself as an advisor for a group of investors. See the examples below.

1. Bankruptcy prediction: Which metrics (financial ratios) are important and why they are relevant to your client’s investment decision.
2. Write a 1500-word report on your analysis. The professional report is to be presented to an intelligent, non-specialist audience. You can use these headings to structure your report.
3. 1. Introduction.
   2. Methodology. -Shows a deep and insightful understanding of the data analytics. An acknowledgement of the data structure such as missing values and the outliers is presence, and appropriate remedies are taken. Multiple estimators are used correctly. Evidence of a strong understanding in the data analytics is demonstrated such as reasons for the choice of the estimators, and data cleaning.
   3. Results, insights, discussion, and recommendation. –Shows a deep and insightful understanding of the data analytics and other key metrics relevant to the choice of both conventional statistical methods and the machine learning algorithms. The discussion and recommendations coherent and substantive.
   4. Limitations and conclusion.
   5. References.

Your report is intended for managerial level decision makers. They don’t need standardised beta and p-values. They need actionable results. Include persuasive data visualisation where necessary

6- 8 references including my 2 references APA

KNN model result

Random Forest

Neural Network

Confusion matrix NN

Confusion matrix – Random forest

Confusion matrix – KNN

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