Use this format (Don’t remove Project 0)
Project # 0 Group members:
Last name, First name
Last name, First name
Last name, First name
Last name, First name
Title: Making a String Telephone
Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).
Project # 1 Group members:
Title: RSNA Pneumonia Detection Challenge
Our team’s project is the RSNA Pneumonia Detection Challenge from Kaggle competition. The primary goal of this project is to develop a machine learning tool to detect patients with pneumonia based on their chest radiographs (CXR).
Pneumonia is an infection that inflames the air sacs in human lungs which has symptoms such as chest pain, cough, and fever . Pneumonia can be very dangerous especially to infants and elders. In 2015, 920,000 children under the age of 5 died from this disease . Due to its fatality to children, diagnosing pneumonia has a high order. A common method of diagnosing pneumonia is to obtain patients’ chest radiograph (CXR) which is a gray-scale scan image of patients’ chests using x-ray. The infected region due to pneumonia usually shows as an area or areas of increased opacity  on CXR. However, many other factors can also contribute to increase in opacity on CXR which makes the diagnose very challenging. The diagnose also requires highly-skilled clinicians and a lot of time of CXR screening. The Radiological Society of North America (RSNA®) sees the opportunity of using machine learning to potentially accelerate the initial CXR screening process.
For the scope of this project, our team plans to contribute to solving this problem by applying our machine learning knowledge in image processing and classification. Team members are going to apply techniques that include, but are not limited to: logistic regression, random forest, SVM, kNN, CNN, etc., in order to successfully detect CXRs with pneumonia.
 (Accessed 2018, Oct. 4). Pneumonia [Online]. MAYO CLINIC. Available from: https://www.mayoclinic.org/diseases-conditions/pneumonia/symptoms-causes/syc-20354204  (Accessed 2018, Oct. 4). RSNA Pneumonia Detection Challenge [Online]. Kaggle. Available from: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge  Franquet T. Imaging of community-acquired pneumonia. J Thorac Imaging 2018 (epub ahead of print). PMID 30036297
Project # 3 Group members:
Jing Pu Sun
Title: Kaggle Challenge: Quick, Draw! Doodle Recognition Challenge
Our team chose the Quick, Draw! Doodle Recognition Challenge from the Kaggle Competition. The goal of the competition is to build an image recognition tool that can classify hand-drawn doodles into one of the 340 categories.
The main challenge of the project remains in the training set being very noisy. Hand-drawn artwork may deviate substantially from the actual object, and is almost definitively different from person to person. Mislabeled images also present a problem since they will create outlier points when we train our models.
We plan on learning more about some of the currently mature image recognition algorithms to inspire and develop our own model.
Project # 4 Group members:
Title: Exploring Kuzushiji-MNIST, a new classification benchmark
The paper *Deep Learning for Classical Japanese Literature* presents a new classification dataset intended to act as a drop-in replacement for MNIST. The paper authors believe that this dataset is significantly more difficult that MNIST for typical classification methods, while not "capping" performance due to indiscernible objects like Fashion-MNIST might. Goals are to:
- perform survey of typical machine-learning algorithms on Kuzushiji-MNIST compared to both MNIST and Fashion-MNIST
- investigate relevant differences in the structures of the datasets
- assess whether Fashion-MNIST does indeed seem to have a performance cap that can be overcome with Kuzushiji-MNIST
Project # 5 Group members:
Pei Wei, Wang
Title: Kaggle Challenge: Airbus Ship Detection Challenge
Classification has become a more and more eye-catching, especially with the rise of machine learning in these years. Our team is particularly interested in machine learning algorithms that optimize some specific type image classification.
In this project, we will dig into base classifiers we learnt from the class and try to cook them together to find an optimal solution for a certain type images dataset. Currently, we are looking into a dataset from Kaggle: Airbus Ship Detection Challenge.
For us, as machine learning students, we are more eager to help getting a better classification method. By “better”, we mean find a balance between simplify and accuracy. We will start with neural network via different activation functions in each layer and we will also combine base classifiers with bagging, random forest, boosting for ensemble learning. Also, we will try to regulate our parameters to avoid overfitting in training dataset. Last, we will summary features of this type image dataset, formulate our solutions and standardize our steps to solve this kind problems
Hopefully, we can not only finish our project successfully, but also make a little contribution to machine learning research field.
Project # 6 Group members:
Title: Kaggle Challenge: PLAsTiCC Astronomical Classification
We will participate in the PLAsTiCC Astronomical Classification competition featured on Kaggle. We will explore how possible it is classify astronomical bodies based on various factors such as brightness.
These bodies will vary in time and size. Some are unknown! There are over 100 classes that these bodies may be and it will be our job to find the predicted probability for an image to be each class.
Project # 7 Group members:
Title: Quora Insincere Questions Classification
Description: Our group will participate in the featured Kaggle competition of Quora Insincere Questions Classification. For this competition, we should predict wether a question asked on Quora is sincere or not. If the question is insincere, it intends to be a statement rather than look for useful answers, and identified as (target = 1). We will analyze the Quora question text to predict the characteristics of questions and define they are sincere or insincere using Rstudio. Our presentation report will include not only how we've concluded by classifying and analyzing provided data with appropriate models, but also how we performed in the contest.
Project # 8 Group members:
Title: Telecom Customer Churn Prediction
Description: Traditional telecommunication industry is made up of telecommunication companies and internet service providers, which play important role in daily life. It is crucial for the telecommunication companies to analyze and maintain their relationship with existing customers, as well as winning new customers with marketing strategies. However, it costs 5 times as much to attract a new customer than to keep an existing one. Therefore, retaining existing customers and building a loyal relationship are the key concerns for traditional telecommunication companies to stay strong in the competition. This project aims to provide insights for the telecom companies in predicting the chance of a customer leaving the company. We will be applying different classification models such as Random Forest, Gradient boosting, Logistic Regression and XGBoost, and then compare each model's performance.
Project # 9 Group members:
Hassan, Ahmad Nayar
Melek, Marina Medhat Rassmi
Title: Quora Insincere Questions Classification: Detect toxic content to improve online conversations
Description: This is a Kaggle Competition. Quora is an online question and answer platfor with content created by its community of users. Quora prides itslef as being a place where users can gain and share knowledge and feel safe doing it. In order to have a safe community, they need to eliminate what they term as "insincere" questions. This competitioon asks Kagglers to develop models that will flag these types of questions given a list of both insincere and sincere questions.
We intend to use Python and its wide variety of packages as we aim to classify these questions.
Reference:  Kaggle. (2018, Nov 18). Quora Insincere Questions Classification. https://www.kaggle.com/c/quora-insincere-questions-classification
Project # 10 Group members:
Title: Kaggle Competition: Human Protein Atlas Image Classification
Project # 11 Group members:
Title: Kaggle Challenge: Quick, Draw! Doodle Recognition Challenge
Project # 12 Group members:
Project # 13 Group members:
Title: Expanding Neural Netwrok
Description: The goal of our project is to create an expanding neural network algorithm which starts off by training a small neural network then expands it to a larger one. We hypothesize that with the proper expansion method we could decrease training time and prevent overfitting. The method we wish to explore is to link together input dimensions based on covariance. Then when the neural network reaches convergence we create a larger neural network without the links between dimensions and using starting values from the smaller neural network.
Project # 14 Group members:
Title: Application of ML Classification to Cancer Identification
Description: The application of machine learning to cancer classification based on gene expression is a topic of great interest to physicians and biostatisticians alike. We would like to work on this for our final project to encourage the application of proven ML techniques to improve accuracy of cancer classification and diagnosis. In this project, we will use the dataset from Golub et al.  which contains data on gene expression on tumour biopsies to train a model and classify healthy individuals and individuals who have cancer.
One challenge we may face pertains to the way that the data was collected. Some parts of the dataset have thousands of features (which each represent a quantitative measure of the expression of a certain gene) but as few as twenty samples. We propose some ways to mitigate the impact of this; including the use of PCA, leave-one-out cross validation, or regularization.
Project # 15 Group members:
Title: Google Analytics Customer Revenue Prediction  - A Kaggle Competition
Description: Guess which cabin class in airlines is the most profitable? One might guess economy - but in reality, it's the premium classes that show higher returns. According to research conducted by Wendover productions , despite having less than 50 seats and taking up more space than the economy class, premium classes end up driving more revenue than other classes.
In fact, just like airlines, many companies adopt the business model where the vast majority of revenue is derived from a minority group of customers. As a result, data-intensive promotional strategies are getting more and more attention nowadays from marketing teams to further improve company returns.
In this Kaggle competition, we are challenged to analyze a Google Merchanidize Store's customer dataset to predict revenue per customer. We will implement a series of data analytics methods including pre-processing, data augmentation, and parameter tuning. Different classification algorithms will be compared and optimized in order to achieve the best results.
 Kaggle. (2018, Sep 18). Google Analytics Customer Revenue Prediction. Retrieved from https://www.kaggle.com/c/ga-customer-revenue-prediction
 Kottke, J (2017, Mar 17). The economics of airline classes. Retrieved from https://kottke.org/17/03/the-economics-of-airline-classes
Project # 16 Group members:
Wang, Yu Hao
Title: Google Analytics Customer Revenue Prediction - A Kaggle Competition
The 80/20 rule has proven true for many businesses–only a small percentage of customers produce most of the revenue. As such, marketing teams are challenged to make appropriate investments in promotional strategies.
RStudio, the developer of free and open tools for R and enterprise-ready products for teams to scale and share work, has partnered with Google Cloud and Kaggle to demonstrate the business impact that thorough data analysis can have.
In this competition, you’re challenged to analyze a Google Merchandise Store (also known as GStore, where Google swag is sold) customer dataset to predict revenue per customer. Hopefully, the outcome will be more actionable operational changes and a better use of marketing budgets for those companies who choose to use data analysis on top of GA data.
we will test a variety of classification algorithms to determine an appropriate model.
Project # 17 Group Members:
Jiang, Ya Fan
Hu, Jerry Jie
Title: Humpback Whale Identification
Description: We analyze Happywhale’s database of over 25,000 images, gathered from research institutions and public contributors to classify each whale to its identification based on its tail image.
Project # 18 Group Members:
Title: Two Sigma: Using News to Predict Stock Movements
Description: Use news analytics to predict stock price performance. This is subject to change.
Project # 19 Group Members:
Yan Yu Chen
Description: Our team presents the Unsupervised Lexicon-Based Sentiment Topic Model (ULSTM) as a sentiment analysis model for reviews on the popular crowd-sourced review forum Yelp. The model applies an unsupervised learning since the supervised method has many constraints. Furthermore, instead of employing an existing sentiment lexicon, we developed a sentiment dictionary using the linguistic corpus WordNet; the self-defined lexicon allows more targeted scoring towards the evaluated dataset. Finally, the ULSTM adopts the Latent Dirichlet Allocation model to find the most mentioned topics in reviews for individual businesses.
Dataset: Yelp Review Dataset from Kaggle
Project # 20 Group Members:
Dong, Yongqi (Michael)
Title: Kaggle--Two Sigma: Using News to Predict Stock Movements
Description: The movement in price of a trade-able security, or stock, on any given day is an aggregation of each individual market participant’s appraisal of the intrinsic value of the underlying company or assets. These values are primarily driven by investors’ expectations of the company’s ability to generate future free cash flow. A steady stream of information on the state of macro and micro-economic variables which affect a company’s operations inform these market actors, primarily through news articles and alerts. We would like to take a universe of news headlines and parse the information into features, which allow us to classify the direction and ‘intensity’ of a stock’s price move, in any given day. Strategies may include various classification methods to determine the most effective solution.
Project # 21 Group members:
Title: Image Segmentation with Capsule Networks using CRF loss
Description: Investigate the impact in changing loss function/regularizers on image segmentation tasks with capsule networks.
Project # 22 Group Members:
Lee, Yu Xuan
Heng, Tsen Yee
Title: Wine Rating Prediction
Description: Predict the rating of the bottles of wine with the help of machine learning. With the variables from the datasets of the wine review which we found in kaggle, we are able to show that different points, price and the year of the production of the wine are very crucial in determining the value of the bottle of wine. The formula of finding the price increased per point for the wine is found from www.vivino.com. From the information we have, we are able to determine which wine is worth to buy!
Project # 23 Group Members:
Malek Mohammadi, Saber
Title: Human Protein Atlas Image Classification
Description: The Human Protein Atlas is a Sweden-based initiative aimed at mapping all human proteins in cells, tissues and organs.
Project # 24 Group Members:
Title: Kaggle Competition: Quora Insincere Questions Classification
Description: Quora is a question-and-answer website where users can ask questions and share opinions. For the company, one key challenge is to identify those insincere questions, which are defined as those founded upon false premises, or that intend to make a statement rather than look for helpful answers. This report is about classifying Quora questions into "Sincere" and "Insincere". The data used in this project was prepared by Quora and can be found on kaggle website. We tried Bi-GRU and Capsule Network model, along with blend of LSTMs and CNN model. Experiments have demonstrated that they have the similar performance.