Knorth - MachineLearning
type: channel
link: https://littleknorth.xyz/index.php/tag/MachineLearning/
items: 预测股价
items: Weather_Classification
items: IMI BigDataAIHUB Case Competition
items: CycleGan人物动漫化
items: Predicting the Rossmann Store Sales Using Different Models in R --- Kaggle IN class Project

预测股价
type: item
date: 2023-08-13T08:44:19+08:00
description: 项目地址：https://github.com/ChenhaoZhu/multiAssets_prediction.git项目概括：拿着四个公司的过去股价信息使用transformer模型进行预测。很简单的部署，很适合初学者。

Weather_Classification
type: item
date: 2023-08-11T21:25:00+08:00
description: Weather_Classification项目概括：运用Pytorch训练天气分类，适用于开始学习深度学习的小白，可以给他们一个上手的项目去锻炼自己训练模型。最后的测试环节使用Yotube视频并使用cv2库去逐帧解帧成图片。项目代码：https://github.com/ChenhaoZhu/Weather_Classification.gitOur data is found on kaggle, and here is the kaggle dataset link : https://www.kaggle.com/datasets/vijaygiitk/multiclass-weather-datasetAlso, we upload the kaggle dataset into our drive and here is the shared drive link : https://drive.google.com/drive/folders/1vBxCKRGNq8gs9xU6i3F4AjWyZzAuMQmn?usp=sharingOur group intends to classify weather images first, and then convert severe weather images into clear images that are easier to identify. Our project has a wide range of application scenarios like autonomous driving. Specifically, Severe weather phenomena have various negative effects on transportation. As a source of information for vehicle sensors, the state of the environment is directly influenced by weather conditions. For camera-led multi-sensor fusion system which is one of the mainstream, it is particularly important to recognize the weather through images and obtain surrounding information based on clear pictures.For the classification part, we tried three different networks and chose the one that performed the best which is resnet101. Firstly, we dowanload data from kaggle and divide labeled data into 5 classes which is Sunrise, Shine, Rainy, Foggy, Cloudy. This is what the raw data looks like. We split the data into training set and validation set in a ratio of 2:1 and Resize image into 255:255. For training and validation set, the input is pictures and the output is the probability that the image belongs to each class.For practicality, we use videos recorded by in-car cameras downloaded from youtube as our test data. We use the code to capture a picture of the video every 30s, and output the probability of belonging to each weather based on the screenshot.For the defrog and derain part, unfortunately, this part is quite challenging for us, we used cycle gan but did not get good resul…

IMI BigDataAIHUB Case Competition
type: item
date: 2023-08-11T21:16:00+08:00
description: IMI BigDataAIHUB Case Competition下载比赛规则与我们小组的团队代码：https://github.com/ChenhaoZhu/detecting_FinancialCrime.git比赛信息概述：二分类与多分类，同时帮助加拿大ScociaBank银行寻找潜在的洗钱组织，该竞赛项目很适合给数据科学的小白或者刚刚入门者来尝试。MotivationFinancial Institutions are used by criminals to “legitimize” money generated from criminal activities, hence banks are expected by society and regulators to act proactively in detecting and stopping criminals from using the financial system to access their money through effective monitoring and reporting to authorities.ComplicationNumber of clients and transaction volume are large and only expected to grow in the futureDiminishing returns of adding more headcountCriminals are constantly innovating on ways to use financial system to “legitimize” their fundsCase GoalUse suitable data analytics tools and techniques to help Scotiabank detect financial crimes:Find known high risk people in our customer base using public dataScore clients according to their likelihood of being involved in Money Laundering using transactional dataEnhance scoring and visualize networks using connections between clientsCase TasksName Screening: Detect 50 Bad Actors in Scotiabank's customer base using public data sources.Risking Rating: Classify customers into Low, Medium and High risk, and predict Bad Actors.Improve Model using Graph Data: Add customer connections information to improve risk rating model or use a graph model directly.Data SourcesRaw DataUofT_nodes.csv: contains KYC, Transactional data and Risk RatingUofT_edges.csv: contains connections between customers i.e. amount of money sent via EMT from one customer (source) to another (target)UofT_occupation_risk.csv: contains mapping between an occupation (code) to their risk level of being involves in financial crimestargets.simple.csv: contains a list of Bad Actors from https://www.opensanctions.org/datasets/default/Intermediate Datamatches_fuzzy.csv: generated from case.ipynb, contains potential Customer-Bad Actor matches, based on names and birt…

CycleGan人物动漫化
type: item
date: 2023-08-11T05:31:00+08:00
description: human_to_cartoon项目概括：人物动漫化与动漫人物化，使用的算法是CycleGan，过段时间可以出一篇关于CycleGan的解释博客，代码是完整的python文件包含训练与测试代码。由于训练的图片数据太大了，github上传不了不过大家可以去网上搜索CycleGan，有不少相关的数据集可以提供下载。这个项目适合准备入行AIGC行业的小白，在他们学习扩散模型前可以先试试cyclegan算法自己来实现。项目代码：https://github.com/ChenhaoZhu/human_to_cartoon.gitThis is a small project that transforming human pictures into cartoon images, and also capable of transforming cartoon images into human images. The methods applied is CycleGAN. CycleGAN is primarily used to transform domain of an image into another domain. In this project, we trained two generators and two discriminators to apply cyclegan. Because the training time takes too long, our model does not perform very well on human-cartoon transformation. However, given more time, we will train more and make generator better for transformation. Human :Cartoon(Transformed) :Cartoon :Human(Transformed) :

Predicting the Rossmann Store Sales Using Different Models in R --- Kaggle IN class Project
type: item
date: 2021-03-31T16:40:00+08:00
description: OK, Let us Start our Project ----- Predicting the SalesStep 0 : Define the Root Mean Square Function for checking my Model in the future.Step 1 : Loading the Data Set and Combine them correspondingly.It is Always important to know what kind of data we are dealing withUse summary(train) to see, and the result is below:Step 2 : Explorative Data analysis. Take a closer look at the data we have.Now we could First Make a Huge Graph containing all possible variables, VS Sales ;It seems that “Customers” has a positive relation with Sales; However, Test Data does not have “Customers”. Under this condition, I think deleting “Customers” in the future modeling will be helpful;From the Graph, it is easy to see that most of our features are categorical features;There are many interesting points I noticed. First, The Sales will be greater on weekday than weekend, as we could see that Sunday has the lowest Sales; Second, Holiday will influence Sales a lot. How About other Variables ? Let us see whether “StoreType” has anything interesting.It seems that “StoreType a “ will have better possibility to get higher sales. Moreover, check “CompetitionDistance” with “StoreType”:The Plot tells us that Higher Sales dependent on Smaller Distance with StoreType “a”; So, ‘CompetitionDistance’, “StoreType” will be crucial features for modeling.Step 3 : Transforming the DataWe could define “week”, “Day”, “Month” using “Date”;We could make Dummy Variables using “StateHoliday”, “StoreType”;Also, we could create “WeekEnd” using “DayOfWeek”;According to any possible holidays, we combine them into “HolidayT”;Using “HolidayT” and “CompetitionDistance”, create “DistanceHoliday”;Next, we need to delete some useless variables;Then we have transformed our data to be available in Modeling !Step 4 : Modeling As we do not want “Customers”, and we need to create data set suitable to different models;The First Model : Linear Regression + Lasso Regression + Ridge RegressionFor Linear Regression Model, we could …