Eric (ewh73), Enya (eez25), and Varun (vm324)
eric-w-h / orie4741_project Goto Github PK
View Code? Open in Web Editor NEWEric, Enya, Varun. Beating the bookies!
Eric, Enya, Varun. Beating the bookies!
This team aimed to use machine learning to choose a best betting strategy. They try SVM's with various kernels, and various tree ensembles, including XGBoost and CatBoost. All of their models outperform the simplest betting strategy by over half. However, they were not able to use their model to make turn a profit.
Three things I liked
Couple things to improve next time
Really great job! Good luck with finals and have a good break!
The goal of this group's project is to discover factors that contribute to a country's GDP and how to leverage them for growth. They plan to go about this by using a dataset from the World Bank, which includes 1443 variables over a 60 year span. The groups intends to use these variables to find factors that hold the most potential for a country's growth.
Some things I liked:
Some things to add:
The purpose of this project is to analyze data from the World Bank World Development Indicators to help countries optimize growth. They will use features including debt, imports by percent of GDP, health expenditure per capita, and the GINI Index. Using this data, they hope to predict rankings of most popular imports and exports and GDP.
Nice job overall, good luck!
Using data from NFL teams and betting sites, this project seeks to predict the winnning team of an NFL match in order to develop profitable betting strategies.
Things i liked:
Things for improvement
Hi group,
This project is set to find hidden indicator of GDP growth and international commerce trend. It is using economic indicator data from the World Bank, and the data comes as continuous variables. Their current plan is to use mainly regression to identify the most influential factor contributing to country’s growth potential.
3 things I liked:
Area of Improvement:
Good luck!
The group tries to predict football game results and win money in sports betting. They have successfully preprocessed a long time series of teams and matches and used such data to build several models including SVM and tree-based models. Although the final model still could not make a profit, these models performed significantly better than the baseline model of betting the favored team.
3 things that interested me:
Things to work on:
This project is about analyzing country GDP and identifying features indicative of stimulating/restricting growth. The objective of the project is to find opportunities for growth and create informed advisory/decision for better budgeting. The data that will be used comes from the World Bank World Development.
I like the project first at a broad level because it feels very relevant/applicable to an actual real life project and something that economic consultants may be tasked with. The project idea also feels very grounded in purpose and the "why" part seems very fulfilled/answered.
Three areas for improvement:
This project is about economic growth in different countries. The goal is to gain information on how countries can manage resources to promote economic growth. The group will use data from the World Bank World Development Indicators including each country’s debt, health expenditure per capita, GINI index and more over a 60 year span from 1960-2020.
I like the idea of using this data to learn how different countries might be able to better manage their resources to improve their economic growth. This seems like a very applicable idea, but it may be difficult to implement in practice. The idea for handling missing data on a case because basis for features is good since it is likely that some features with many missing entries are unnecessary and can just be dropped. I also like your idea of looking at each individual country and attempting to predict the most important factors in its development. With the missing data, I think it is important to consider how you will impute missing data for features that seem veery relevant, as well as for possibly missing an entry for a given year for a country. There may not be very many places where this occurs, but if it is not handled carefully it could drastically impact your models. I also think may be a good idea to split your data into training and validation sets based on year, with a range of the most recent years (like 2005-2020) as your validation data. This will allow you to better evaluate how your model works for ranges of time it has not been trained on, giving a better idea as to how it will generalize to predicting future economic growth. Lastly, I think may be important to consider how different countries import/export needs vary especially when attempting to predict the rankings of the most popular imports and exports. Predicting the specific goods or types of goods which countries import and export the most may be less important than the overall quantities of all their imports and exports so maybe consider combining features on specific imports/exports into features for overall imports/exports. Overall, I think this will be an interesting project and look forward to seeing the final product. Good Luck!
The project tis about analysis on the GDP growth and finding factors that may contribute to the changes of the GDP. The problem chooses a vast dataset over the last 60 years with appropriate features that may achieve the group's goals.
The things I liked:
Like I mentioned, I think the dataset is massive and contains a lot of information that the group could do a lot of work on. I think this could help out with prevention from neglecting any factors that may potentially yield unwanted results/data or overfitting.
The visualizations are clear and easy to take in as someone who views them for the first time. Also, the the explanation is adequate so that we can understand the direction of the group's attempts.
Detailed explanation on prevention of overfitting/underfitting is an essential part of the data analysis and the group does this well.
Thins for improvements:
I think there could be more visualizations, especially ones on what we have learned on the future. I think some of the points mentioned in the project could feel quite intuitiive.
For the feature selection , I think the heat map that shows the relationship and the correlation could have been a better version to use for feature selection.
Hopefully, a more clear explanation on the direction the group is headed? Kinda seems a bit too simple and similar to the project itself.
Overall, I liked it a lot and see a lot of potential! Good luck
Dear Valued Employees,
I have read your proposal and find your request very intriguing. From my understanding you want to analyze particular countries' growth opportunities in order to discover ways that these institutions can optimize their yearly budgeting. By looking at GDP statistics from a range of countries that go back several decades you hope to gain insight into what covariates contribute the most to a country's growth and then once these features are known you can suggest changes to policy makers so that the budget can be more heavily weighted in those particular areas. The data you will be using to make this analysis is from The World Bank, an international financial institution that provides loans and grants to a range of different countries.
There are 3 aspects of your proposal that really inspired me. The first is the source of your data. The World Bank is the foremost authority when gathering information on the GDP of the nations of the world and if you do manage to extract information from the dataset, you know it is not because of initial data manipulation. In addition, I have always been a firm believer in the notion that if you're going to do something, do something that matters. The topic you choose to talk about is a very pressing one that if you could make a breakthrough in, would positively affect the lives of millions of individuals. The last aspect of this proposal that caught my eye was your method plan. It appears to me that you have a general idea of the path you want to take.
However, there are 3 facets of this project that give me hesitation. The first is the general outline of the dataset. This dataset is extremely big. Narrowing down covariates that will help you answer the proposed research question will not be an easy feat. You need to find a balance between covariates that are specific to certain areas of a budget, but broad enough so that they apply to many different nations. My second concern is the size of the problem you are trying to answer. Each country has very unique geographical, populational, political ect. concerns. Many countries are extremely different! Trying to find optimizations of a country's GDP based on other countries may not work as well as one might expect. It will be very hard to train a classifier/ML algorithm that can generally suggest budgetary moves for a range of different countries. Finally, my last concern is the format of your proposal. I did not get the sense that you were asking permission from me more than telling me which greatly offends me as your boss.
Overall there are a few aspects of this project that need more flushing out but I could see myself in the future approving this proposal.
Sincerely,
Your Boss
This group uses data from the Pro Football Archives and Odds Portal to predict score betting. They use SVCs with different kernels, and three different Cs, and gradient-boosted decision tree ensemble. Although their model is not yet a weapon of math destruction, as it doesn’t yet turn a profit, the analysis and model fitting is thorough and solid.
I like how the reports illustrate what the odds and betting ideas are, since it is not a very familiar topic to me. And the data processing graphs are easy to read. Also, they explain why the specific models are chosen.
However, there are still some spaces for improvement: it will be better if you could make the model results graph easier to read and emphasis ethe information’s we want to draw from the graphs. Also, analyzing the bias variance tradeoff for each model from model building perspective a bit more. Each section could be numbered so that the paper is easier to read.
This project aims to develop a model that determines the influence of various factors on a country's GDP.
What I liked:
Areas of Improvement:
The project is about using a dataset to analyze the patterns of international trade and global economic development. A dataset from the World Bank is selected, including features such as Economic Policies, Financial Sector Performance, Gender, Health, Infrastructure, etc. The project aims to determine opportunities for growth for a country so a country can make better budgeting decisions.
Things I Like:
Areas for Improvement:
This project is going to predict the winner in NFL match in order to develop profitable betting strategies. They tried various machine learning methods, finally develop the model although it doesn't make profit.
Things I liked:
Things need to improve:
Projecting GDP
==============
Summary
-------
This project is about predicting increases and
decreases in the GDP of countries around the world.
The team seeks to analyze factors from trading
patterns to education in order to develop a metric
by which a country's future GDP can be measured.
The end goal of their analysis is to potentially
help a country understand its assets and methods
to improve its growth.
Things I liked
--------------
1. I liked that the introduction was concise, and
described not only the analysis that was to be done
but also its purpose. The importance of the questions
being answered by the analysis was very evident in
the language used, although it avoided being overly
flowery so as to prevent the reader's eyes from
glazing over.
2. I also liked the use of least squares along with
the visualization that accompanied it. It clearly
demonstrated to the reader the degree of inefficacy
the features had in demonstrating trends in the data.
3. The separation of categories from each other was
good to see, and made it easy to parse through exactly
where the requirements for the midterm report were met.
Areas for improvement
---------------------
1. The report mentioned the fact that some of the data
were missing, but didn't described how the missing data
were being dealt with outside of choosing variables with
low percentages of missing data for the analysis.
2. It seemed like there was room for more elaboration.
Although the report did seem thorough, a change in the
overall scaling of the project could have made it seem
more complete in order to fit the page limit.
3. There were no histograms of the dataset made on the
report, despite the fact that they were suggested in the
task description: "Include a few histograms or other
descriptive statistics about the data."
Overall great job!
The goal of the project is to analyse which factors that contribute to the GDP of a country. This is useful in order to identify patterns in economic growth and opportunitites for growth. The data is from the world bank and contains features such as trading patterns, infrastructure, education etc.
Good:
Questions/improvemennts:
Overall, good job
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