http://wiki.math.uwaterloo.ca/statwiki/api.php?action=feedcontributions&user=Y2639li&feedformat=atomstatwiki - User contributions [US]2024-03-29T07:29:39ZUser contributionsMediaWiki 1.41.0http://wiki.math.uwaterloo.ca/statwiki/index.php?title=F21-STAT_441/841_CM_763-Proposal&diff=51225F21-STAT 441/841 CM 763-Proposal2021-12-11T01:37:32Z<p>Y2639li: </p>
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<div>Use this format (Don’t remove Project 0)<br />
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Project # 0 Group members:<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Title: Making a String Telephone<br />
<br />
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).<br />
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Project # 1 Group members:<br />
<br />
Feng, Jared<br />
<br />
Huang, Xipeng<br />
<br />
Xu, Mingwei<br />
<br />
Yu, Tingzhou<br />
<br />
Title: Patch-Based Convolutional Neural Network for Cancers Classification<br />
<br />
Description: In this project, we consider classifying three classes (tumor types) of cancers based on pathological data. We will follow the paper ''Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification''.<br />
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Project # 2 Group members:<br />
<br />
Anderson, Eric<br />
<br />
Wang, Chengzhi<br />
<br />
Zhong, Kai<br />
<br />
Zhou, Yi Jing<br />
<br />
Title: Data Poison Attacks<br />
<br />
Description: Attempting to create a successful data poisoning attack<br />
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Project # 3 Group members:<br />
<br />
Chopra, Kanika<br />
<br />
Rajcoomar, Yush<br />
<br />
Bhattacharya, Vaibhav<br />
<br />
Title: Cancer Classification<br />
<br />
Description: We will be classifying three tumour types based on pathological data. <br />
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Project # 4 Group members:<br />
<br />
Li, Shao Zhong<br />
<br />
Kerr, Hannah <br />
<br />
Wong, Ann Gie<br />
<br />
Title: Classification of text<br />
<br />
Description: Being to automatically grade answers on tests can save a lot of time and teaching resources. But unlike a multiple-choice format where grading can be automated, the other formats involving text answers is more through in testing knowledge but still requires human evaluation and marking which is a bottleneck of teaching resources and personnel on a large scale with thousands of students. We will use classification techniques and machine learning to develop an automated way to predict the rightness of text answers with good accuracy that can be used by and suppport graders to reduce the time and manual effort needed in the grading process.<br />
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Project # 5 Group members:<br />
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Chin, Jessie Man Wai<br />
<br />
Ooi, Yi Lin<br />
<br />
Shi, Yaqi<br />
<br />
Ngew, Shwen Lyng<br />
<br />
Title: The Application of Classification in Accelerated Underwriting (Insurance)<br />
<br />
Description: Accelerated Underwriting (AUW), also called “express underwriting,” is a faster and easier process for people with good health condition to obtain life insurance. The traditional underwriting process is often painful for both customers and insurers. From the customer's perspective, they have to complete different types of questionnaires and provide different medical tests involving blood, urine, saliva and other medical results. Underwriters on the other hand have to manually go through every single policy to access the risk of each applicant. AUW allows people, who are deemed “healthy” to forgo medical exams. Since COVID-19, it has become a more concerning topic as traditional underwriting cannot be performed due to the stay-at-home order. However, this imposes a burden on the insurance company to better estimate the risk associated with less testing results. <br />
<br />
This is where data science kicks in. With different classification methods, we can address the underwriting process’ five pain points: labor, speed, efficiency, pricing and mortality. This allows us to better estimate the risk and classify the clients for whether they are eligible for accelerated underwriting. For the final project, we use the data from one of the leading US insurers to analyze how we can classify our clients for AUW using the method of classification. We will be using factors such as health data, medical history, family history as well as insurance history to determine the eligibility.<br />
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Project # 6 Group members:<br />
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Wang, Carolyn<br />
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Cyrenne, Ethan<br />
<br />
Nguyen, Dieu Hoa<br />
<br />
Sin, Mary Jane<br />
<br />
Title: Pawpularity (PetFinder Kaggle Competition)<br />
<br />
Description: Using images and metadata on the images to predict the popularity of pet photos, which is calculated based on page view statistics and other metrics from the PetFinder website.<br />
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Project # 7 Group members:<br />
<br />
Bhattacharya, Vaibhav<br />
<br />
Chatoor, Amanda<br />
<br />
Prathap Das, Sutej<br />
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Title: PetFinder.my - Pawpularity Contest [https://www.kaggle.com/c/petfinder-pawpularity-score/overview]<br />
<br />
Description: In this competition, we will analyze raw images and metadata to predict the “Pawpularity” of pet photos. We'll train and test our model on PetFinder.my's thousands of pet profiles.<br />
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Project # 8 Group members:<br />
<br />
Yan, Xin<br />
<br />
Duan, Yishu<br />
<br />
Di, Xibei<br />
<br />
Title: The application of classification on company bankruptcy prediction<br />
<br />
Description: TBD<br />
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Project # 9 Group members:<br />
<br />
Loke, Chun Waan<br />
<br />
Chong, Peter<br />
<br />
Osmond, Clarice<br />
<br />
Li, Zhilong<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
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Project # 10 Group members:<br />
<br />
O'Farrell, Ethan<br />
<br />
D'Astous, Justin<br />
<br />
Hamed, Waqas<br />
<br />
Vladusic, Stefan<br />
<br />
Title: Pawpularity (Kaggle)<br />
<br />
Description: Predicting the popularity of animal photos based on photo metadata<br />
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Project # 11 Group members:<br />
<br />
JunBin, Pan<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
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Project # 12 Group members:<br />
<br />
Kar Lok, Ng<br />
<br />
Muhan (Iris), Li<br />
<br />
Wu, Mingze<br />
<br />
Title: NFL Health & Safety - Helmet Assignment competition (Kaggle Competition)<br />
<br />
Description: Assigning players to the helmet in a given footage of head collision in football play.<br />
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Project # 13 Group members:<br />
<br />
Livochka, Anastasiia<br />
<br />
Wong, Cassandra<br />
<br />
Evans, David<br />
<br />
Yalsavar, Maryam<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
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Project # 14 Group Members:<br />
<br />
Zeng, Mingde<br />
<br />
Lin, Xiaoyu<br />
<br />
Fan, Joshua<br />
<br />
Rao, Chen Min<br />
<br />
Title: TBD<br />
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Description: TBD<br />
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Project # 15 Group Members:<br />
<br />
Huang, Yuying<br />
<br />
Anugu, Ankitha<br />
<br />
Dave, Meet Hemang<br />
<br />
Chen, Yushan<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
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Project # 16 Group Members:<br />
<br />
Wang, Lingshan<br />
<br />
Li, Yifan<br />
<br />
Liu, Ziyi<br />
<br />
Title: Implement and Improve CNN in Multi-Class Text Classification<br />
<br />
Description: We are going to apply Bidirectional Encoder Representations from Transformers (BERT) to classify real-world data (application to build an efficient case study interview materials classifier) and improve it algorithm-wise in the context of text classification, being supported with real-world data set. With the implementation of BERT, it allows us to further analyze the efficiency and practicality of the algorithm when dealing with imbalanced dataset in the data input level and modelling level.<br />
The dataset is composed of case study HTML files containing case information that can be classified into multiple industry categories. We will implement a multi-class classification to break down the information contained in each case material into some pre-determined subcategories (eg, behavior questions, consulting questions, questions for new business/market entry, etc.). We will attempt to process the complicated data into several data types(e.g. HTML, JSON, pandas data frames, etc.) and choose the most efficient raw data processing logic based on runtime and algorithm optimization.<br />
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Project # 17 Group members:<br />
<br />
Malhi, Dilmeet<br />
<br />
Joshi, Vansh<br />
<br />
Syamala, Aavinash <br />
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Islam, Sohan<br />
<br />
Title: Kaggle project: PetFinder.my - Pawpularity Contest<br />
<br />
Description: In this competition, we will analyze raw images provided by PetFinder.my to predict the “Pawpularity” of pet photos.<br />
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Project # 18 Group members:<br />
<br />
Yuwei, Liu<br />
<br />
Daniel, Mao<br />
<br />
Title: Sartorius - Cell Instance Segmentation (Kaggle) [https://www.kaggle.com/c/sartorius-cell-instance-segmentation]<br />
<br />
Description: Detect single neuronal cells in microscopy images<br />
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Project #19 Group members:<br />
<br />
Samuel, Senko<br />
<br />
Tyler, Verhaar<br />
<br />
Zhang, Bowen<br />
<br />
Title: NBA Game Prediction<br />
<br />
Description: We will build a win/loss classifier for NBA games using player and game data and also incorporating alternative data (ex. sports betting data).<br />
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Project #20 Group members:<br />
<br />
Mitrache, Christian<br />
<br />
Renggli, Aaron<br />
<br />
Saini, Jessica<br />
<br />
Mossman, Alexandra<br />
<br />
Title: Classification and Deep Learning for Healthcare Provider Fraud Detection Analysis<br />
<br />
Description: TBD<br />
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Project # 21 Group members:<br />
<br />
Wang, Kun<br />
<br />
Title: TBD<br />
<br />
Description : TBD<br />
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Project # 22 Group members:<br />
<br />
Guray, Egemen<br />
<br />
Title: Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network<br />
<br />
Description : I will build a prediction system to predict road signs in the German Traffic Sign Dataset using CNN.<br />
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Project # 23 Group members:<br />
<br />
Bsodjahi<br />
<br />
Title: Modeling Pseudomonas aeruginosa bacteria state through its genes expression activity<br />
<br />
Description : Label Pseudomonas aeruginosa gene expression data through unsupervised learning (eg., EM algorithm) and then model the bacterial state as function of its genes expression</div>Y2639lihttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=50103stat441F212021-11-10T00:25:12Z<p>Y2639li: </p>
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<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 || Ali Ghodsi || || || || ||<br />
|-<br />
|Week of Nov 16 || Jared Feng, Xipeng Huang, Mingwei Xu, Tingzhou Yu|| || Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification || [http://proceedings.mlr.press/v139/bai21c/bai21c.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] ||<br />
|-<br />
|Week of Nov 16 || Kanika Chopra, Yush Rajcoomar || || Automatic Bank Fraud Detection Using Support Vector Machines || [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.863.5804&rep=rep1&type=pdf Paper] || ||<br />
|-<br />
|Week of Nov 22 || Zeng Mingde, Lin Xiaoyu, Fan Joshua, Rao Chen Min || || || || ||<br />
|-<br />
|Week of Nov 22 || Justin D'Astous, Waqas Hamed, Stefan Vladusic, Ethan O'Farrell || || A Probabilistic Approach to Neural Network Pruning || [http://proceedings.mlr.press/v139/qian21a/qian21a.pdf] || ||<br />
|-<br />
|Week of Nov 22 || Cassandra Wong, Anastasiia Livochka, Maryam Yalsavar, David Evans || || Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification || [https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Hou_Patch-Based_Convolutional_Neural_CVPR_2016_paper.pdf Paper] || ||<br />
|-<br />
|Week of Nov 22 || Jessie Man Wai Chin, Yi Lin Ooi, Yaqi Shi, Shwen Lyng Ngew || || || || ||<br />
|-<br />
|Week of Nov 22 || Eric Anderson, Chengzhi Wang, Kai Zhong, YiJing Zhou || || || || ||<br />
|-<br />
|Week of Nov 29 || Ethan Cyrenne, Dieu Hoa Nguyen, Mary Jane Sin, Carolyn Wang || || || || ||<br />
|-<br />
|Week of Nov 22 || Ann Gie Wong, Curtis Li, Hannah Kerr || || The Detection of Black Ice Accidents for Preventative Automated Vehicles Using Convolutional Neural Networks || [https://www.mdpi.com/2079-9292/9/12/2178/htm Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Detection_of_Black_Ice_Accidents_Using_CNNs&fbclid=IwAR0K4YdnL_hdRnOktmJn8BI6-Ra3oitjJof0YwluZgUP1LVFHK5jyiBZkvQ Summary] ||<br />
|-<br />
|Week of Nov 22 || Yuwei Liu, Daniel Mao|| || Another Look At Distance-Weighted Discrimination || [http://users.stat.umn.edu/~wang3660/papers/kerndwd.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Another_look_at_distance-weighted_discrimination Summary] ||<br />
|-<br />
|Week of Nov 22 || Lingshan Wang, Yifan Li, Ziyi Liu || || Understanding Convolutional Neural Networks for Text Classification || [https://arxiv.org/pdf/1809.08037.pdf Paper] || ||<br />
|-</div>Y2639lihttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=F21-STAT_441/841_CM_763-Proposal&diff=49977F21-STAT 441/841 CM 763-Proposal2021-10-08T15:52:29Z<p>Y2639li: </p>
<hr />
<div>Use this format (Don’t remove Project 0)<br />
<br />
Project # 0 Group members:<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Title: Making a String Telephone<br />
<br />
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).<br />
<br />
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Project # 1 Group members:<br />
<br />
Feng, Jared<br />
<br />
Huang, Xipeng<br />
<br />
Xu, Mingwei<br />
<br />
Yu, Tingzhou<br />
<br />
Title: <br />
<br />
Description:<br />
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Project # 2 Group members:<br />
<br />
Anderson, Eric<br />
<br />
Wang, Chengzhi<br />
<br />
Zhong, Kai<br />
<br />
Zhou, Yi Jing<br />
<br />
Title: Application of Neural Networks<br />
<br />
Description: Using neural networks to determine content/intent of emails.<br />
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Project # 3 Group members:<br />
<br />
Chopra, Kanika<br />
<br />
Rajcoomar, Yush<br />
<br />
Title: Classification<br />
<br />
Description: We will be working on the alternate project that the Professor will release on Sunday<br />
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Project # 4 Group members:<br />
<br />
Zhang, Bowen<br />
<br />
Li, Shaozhong<br />
<br />
Kerr, Hannah<br />
<br />
Wong, Ann gie<br />
<br />
Title: Classification<br />
<br />
Description: TBD<br />
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Project # 5 Group members:<br />
<br />
Chin, Jessie Man Wai<br />
<br />
Ooi, Yi Lin<br />
<br />
Shi, Yaqi<br />
<br />
Ngew, Shwen Lyng<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
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Project # 6 Group members:<br />
<br />
Wang, Carolyn<br />
<br />
Cyrenne, Ethan<br />
<br />
Nguyen, Dieu Hoa<br />
<br />
Sin, Mary Jane<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
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Project # 7 Group members:<br />
<br />
Bhattacharya, Vaibhav<br />
<br />
Chatoor, Amanda<br />
<br />
Prathap Das, Sutej<br />
<br />
Title: PetFinder.my - Pawpularity Contest [https://www.kaggle.com/c/petfinder-pawpularity-score/overview]<br />
<br />
Description: In this competition, we will analyze raw images and metadata to predict the “Pawpularity” of pet photos. We'll train and test our model on PetFinder.my's thousands of pet profiles.<br />
<br />
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Project # 8 Group members:<br />
<br />
Xu, Siming<br />
<br />
Yan, Xin<br />
<br />
Duan, Yishu<br />
<br />
Di, Xibei<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
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Project # 9 Group members:<br />
<br />
Loke, Chun Waan<br />
<br />
Chong, Peter<br />
<br />
Osmond, Clarice<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
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<br />
Project # 10 Group members:<br />
<br />
O'Farrell, Ethan<br />
<br />
D'Astous, Justin<br />
<br />
Hamed, Waqas<br />
<br />
Vladusic, Stefan<br />
<br />
Title: Pawpularity (Kaggle)<br />
<br />
Description: Predicting the popularity of animal photos based on photo metadata<br />
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Project # 11 Group members:<br />
<br />
JunBin, Pan<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
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Project # 12 Group members:<br />
<br />
Kar Lok, Ng<br />
<br />
Muhan (Iris), Li<br />
<br />
Wu, Mingze<br />
<br />
Title: NFL Health & Safety - Helmet Assignment competition (Kaggle Competition)<br />
<br />
Description: Assigning players to the helmet in a given footage of head collision in football play.<br />
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Project # 13 Group members:<br />
<br />
Livochka, Anastasiia<br />
<br />
Wong, Cassandra<br />
<br />
Evans, David<br />
<br />
Yalsavar, Maryam<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
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Project # 14 Group Members:<br />
<br />
Syamala, Aavinash Reddy<br />
<br />
Zhu, Jigang<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
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Project # 15 Group Members:<br />
<br />
Zeng, Mingde<br />
<br />
Lin, Xiaoyu<br />
<br />
Fan, Joshua<br />
<br />
Rao, Chen Min<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
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Project # 16 Group Members:<br />
<br />
Huang, Yuying<br />
<br />
Anugu, Ankitha<br />
<br />
Dave, Meet Hemang<br />
<br />
Chen, Yushan<br />
<br />
Title: TBD<br />
<br />
Description: TBD<br />
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Project # 17 Group Members:<br />
<br />
Wang, Lingshan<br />
<br />
Liu, Ziyi<br />
<br />
Zheng, Hanxi<br />
<br />
Li, Yifan<br />
<br />
Title: Implement and Improve CNN in Multi-Class Text Classification<br />
<br />
Description: We are going to apply Convolutional Neural Network (CNN) to classify real-world data (application to build an efficient insurance contract classifier) and improve CNN algorithm-wise in the context of text classification, being supported with real-world data set. With the implementation of CNN, it allows us to further analyze the efficiency and practicality of the algorithm.<br />
The dataset is composed of insurance contracts containing client and policy information. We will implement a multi-class classification to break down the information contained in each insurance contract into some pre-determined subcategories (eg, short-term renewable/long-term non-renewable). We will attempt to process the complicated data into several data types(e.g. JSON, pandas data frames, etc.) and choose the most efficient raw data processing logic based on runtime and algorithm optimization.</div>Y2639li