Machine Learning Application: Credit Scoring
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1) The document discusses using machine learning techniques like logistic regression, random forest, and K-means clustering to develop a credit scoring model based on financial ratios to predict a company's probability of default. 2) Random forest performed the best with an AUC of 0.87 and high precision and recall, while logistic regression had a AUC of 0.75 but issues with type II errors. 3) K-means clustering had a lower precision predicting defaults but an acceptable F1-score and AUC of 0.80.
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Calculate the “t” value for independent groups for the following data using the formula provided in the attached word document. Using the raw measurement data presented, determine whether or not there exists a statistically significant difference between the salaries of female and male human resource managers using the appropriate t-test. Develop a testable hypothesis, confidence level, and degrees of freedom. Report the required “t” critical values based on the degrees of freedom. Show calculations.
Answer
The null hypothesis tested is
H0: There is no significant difference between the average salaries of female and male human resource managers. (µ1= µ2)
The alternative hypothesis is
H1: There is significant difference between the average salaries of female and male human resource managers. (µ1≠ µ2)
The test statistic used is
12
12
2
~
NN
DM
MM
tt
S
+-
-
=
Where
22
1122
1212
(1)(1)
11
2
DM
NsNs
S
NNNN
éùéù
-+-
=+
êúêú
+-
ëûëû
Here M1 = 62,200, M2 = 63,700
s1 = 9330.95, s2 = 6912.95
N1 = 10, N2 = 10 (See the excel sheet)
Then,
(
)
(
)
22
(101)9330.95(101)6912.95
11
101021010
DM
S
éù
-+-
éù
=+
êú
êú
+-
ëû
êú
ëû
= 3672.267768
Therefore test statistic,
62,20063,700
3672.267768
t
-
=
= -0.408466946
Degrees of freedom = N1 + N2 – 2 = 10 + 10 – 2 = 18
Let the significance level be 0.05.
Rejection criteria: Reject the null hypothesis, if the calculated value of t is greater than the critical value of t at 0.05 significance level.
The critical values can be obtained from the student’s t tables with 18 d.f. at 0.05 significance level.
Upper critical value = 2.1
Lower critical value = -2.1
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Decoding Loan Approval: Predictive Modeling in Action
Delve into the realm of predictive modeling for loan approval. Learn how data science is revolutionizing the lending industry, making the loan approval process faster, more accurate, and fairer. Discover the key factors that influence loan decisions and how predictive modelling is shaping the future of lending. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights
WEKA:Credibility Evaluating Whats Been Learned
- Training and test sets are used to measure classification success rates, with the test set being independent of the training set. The error rate on the training set is optimistic. Cross validation techniques like 10-fold stratified cross validation are used when data is limited.
- True success rates are predicted using properties of statistics and normal distributions. Confidence levels determine the range within which the true rate is expected to lie.
- Techniques like paired t-tests are used to statistically compare the performance of different algorithms or data mining methods. They determine if performance differences are statistically significant.
WEKA: Credibility Evaluating Whats Been Learned
This document discusses various techniques for evaluating machine learning models and comparing their performance, including:
- Measuring error rates on separate test and training sets to avoid overfitting
- Using techniques like cross-validation, bootstrapping, and holdout validation when data is limited
- Comparing algorithms using statistical tests like paired t-tests
- Accounting for costs of different prediction outcomes in evaluation and model training
- Visualizing performance using lift charts and ROC curves to compare models
- The Minimum Description Length principle for selecting the model that best compresses the data
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一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
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如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
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◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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Independent Study - College of Wooster Research (2023-2024)
"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
My study abroad in Bali, Indonesia, inspired this research topic as I noticed how globalization is changing the culture of its people. I learned their language and way of life which helped me understand the beauty and importance of cultural preservation. I believe we could all benefit from learning new perspectives as they could help us ideate solutions to contemporary issues and empathize with others.
Ending stagnation: How to boost prosperity across Scotland
A toxic combination of 15 years of low growth, and four decades of high inequality, has left Britain poorer and falling behind its peers. Productivity growth is weak and public investment is low, while wages today are no higher than they were before the financial crisis. Britain needs a new economic strategy to lift itself out of stagnation.
Scotland is in many ways a microcosm of this challenge. It has become a hub for creative industries, is home to several world-class universities and a thriving community of businesses – strengths that need to be harness and leveraged. But it also has high levels of deprivation, with homelessness reaching a record high and nearly half a million people living in very deep poverty last year. Scotland won’t be truly thriving unless it finds ways to ensure that all its inhabitants benefit from growth and investment. This is the central challenge facing policy makers both in Holyrood and Westminster.
What should a new national economic strategy for Scotland include? What would the pursuit of stronger economic growth mean for local, national and UK-wide policy makers? How will economic change affect the jobs we do, the places we live and the businesses we work for? And what are the prospects for cities like Glasgow, and nations like Scotland, in rising to these challenges?
Fabular Frames and the Four Ratio Problem
Digital, interactive art showing the struggle of a society in providing for its present population while also saving planetary resources for future generations. Spread across several frames, the art is actually the rendering of real and speculative data. The stereographic projections change shape in response to prompts and provocations. Visitors interact with the model through speculative statements about how to increase savings across communities, regions, ecosystems and environments. Their fabulations combined with random noise, i.e. factors beyond control, have a dramatic effect on the societal transition. Things get better. Things get worse. The aim is to give visitors a new grasp and feel of the ongoing struggles in democracies around the world.
Stunning art in the small multiples format brings out the spatiotemporal nature of societal transitions, against backdrop issues such as energy, housing, waste, farmland and forest. In each frame we see hopeful and frightful interplays between spending and saving. Problems emerge when one of the two parts of the existential anaglyph rapidly shrinks like Arctic ice, as factors cross thresholds. Ecological wealth and intergenerational equity areFour at stake. Not enough spending could mean economic stress, social unrest and political conflict. Not enough saving and there will be climate breakdown and ‘bankruptcy’. So where does speculative design start and the gambling and betting end? Behind each fabular frame is a four ratio problem. Each ratio reflects the level of sacrifice and self-restraint a society is willing to accept, against promises of prosperity and freedom. Some values seem to stabilise a frame while others cause collapse. Get the ratios right and we can have it all. Get them wrong and things get more desperate.
New Visa Rules for Tourists and Students in Thailand | Amit Kakkar Easy Visa
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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Machine Learning Application: Credit Scoring
- 1. Machine Learning Application: Credit Scoring Programming Techniques Professor Carlos Costa Master in Mathematical Finance Federico Innocenti 53251 Miguel Albergaria 48547 Claudio Napoli 53358 Iacopo Fiorentino 53315 Lisbon, December 11th 2019
- 2. Context ► The data is collected from Thomson Reuters from firms included in the main stock indexes. ► The goal is to set a score of a company to decide whether to give a loan or not to that firm based on a client’s probability of default. ► For that we compute many ratios and at the end we want to “differentiate winners from losers”.
- 3. Data preparation ► Importing data, checking the type of data and clearing missing values; ► Correlation matrix; ► See how the data is distributed through graphs; ► Rearranging the data clearing very low values and very high values, i.e., outliers. ► After all of that, we did the correlation matrix and graphs again to compare them and to have a better view of our results.
- 4. Modelling data ► Our data doesn´t have a probability of default, so we need to create one. ► In order to compute the machine learning approach we use: ► Supervised learning: logistic regression and random forest ► Unsupervised learning: clustering K-mean ► We decided to use a financial scorecard, in order to give a certain score to different ratios.
- 5. Setting the score ► Relevant ratios: current ratio, debt ratio, equity to asset ratio, debt to equity ratio, return on asset, return on equity, long term coverage ratio and asset turnover ratio.
- 6. ► The company’s goal is to obtain the highest score that we compute in the way showed before. An example of the code is shown here: ► The final score is set by adding all of the “ratios’ scores”.
- 7. Evaluation ► For the evaluation of our model we compute a confusion matrix in order to see the result and have an easier first parametre to compare the three models. ► After setting the score we binarize the score being 1 the lowest probability of default and 0 the highest. We chose as threshold a score of 500 points and then we proceed to the evaluation.
- 8. Logistic Regression ► We leave the set of the logistic regression in default mode with a test size of 0.7. ► The final result is good with a AUC of 0.75, which means that it is a good model distinguishing the given classes. ► But there is a problem! ► The model has a type 2 error. In other words, it predicts 1 but actually is 0. ► So the F1 score (measure of accuracy) is 0.68.
- 9. Random Forest ► In order to optimize the process we put the “number of jobs” 150 and the “number of estimators” is 1 since it is a binomial classification.
- 10. ► This model achieved a really high AUC: 0.87 and a good F1-Score. ► High precision and high recall means low probability of error type I and II.
- 11. K-Mean ► We increased the number of iterations to 400 times in order to optimize this model and to try to get more stable results. ► The main problem with the K-mean clustering model is that it suffers from a low precision predicting the default cases (type I error). ► On the other hand it has an acceptable F1-Score and a AUC of 0.80.
- 12. Conclusions ► The standardization of the ratio and the cleaning of the data gets the models to have a high AUC on the three models. ► The better model is the Random Forest, getting a better AUC result. ► We confirm that machine learning algorithms are really powerful in analysing data and it can be helpful to solve this specific problem.