yczhangcc
Aspiring Data Scientist | AI Enthusiast | Problem Solver | Passionate about turning data into actionable insights and using machine learning to solve problems
University of Texas at AustinAustin, Texas
yczhangcc's Stars
AnmolTomer/cpp_deep_dive
Notes for C++ Deep Dive Course on Udemy by Abdul Bari.
SimplifyJobs/Summer2025-Internships
Collection of Summer 2025 tech internships!
SimplifyJobs/New-Grad-Positions
A collection of full time roles in SWE, Quant, and PM for new grads.
AI4Finance-Foundation/FinGPT
FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
cobiwave/simplefolio
⚡️ A minimal portfolio template for Developers
Yuis1/LLM4Stock
基于LLM的股票投资决策系统
pipiku915/FinMem-LLM-StockTrading
FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
Pranav082001/stock-analyzer-bot
An AI Bot that can help you in doing stock investment by analyzing all the real time as well as historic stock information with the help of LLM
Blokje5/fraud-detection-pipeline
Final project for Udacity Nanodegree
Boese0601/Curriculum-Vitae-Latex
A simple version of CV/Resume for application of Master/Doctoral degree.
AlexTheAnalyst/PortfolioProjects
curiousily/Machine-Learning-from-Scratch
Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.
yazanobeidi/fraud-detection
Credit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook
krishnaik06/Credit-Card-Fraudlent
HanXiaoyang/Kaggle_Titanic
the data and ipython notebook of my attempt to solve the kaggle titanic problem
curiousily/Deep-Learning-For-Hackers
Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)
soumyajit4419/Portfolio
My self coded personal website build with React.js
afshinea/stanford-cs-230-deep-learning
VIP cheatsheets for Stanford's CS 230 Deep Learning
curiousily/Credit-Card-Fraud-Detection-using-Autoencoders-in-Keras
iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data
youngyangyang04/leetcode-master
《代码随想录》LeetCode 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀
DS-XL/ds_2024
Repo For 2024 DS Intro Level
lilianweng/lilianweng.github.io
My personal page
valentineashio/Online-Payments-Fraud-Detection-Dataset-Case-Study
A Data Science/Machine Learning Project. According to Bolster , Global Fraud Index (as at June 2022) is at 10,183 and growing. This is high risk to businesses and customers transacting online. This indicates that traditional rules-based methods of detecting and combating fraud are fast becoming less effective. It becomes imperative for stakeholders to develop innovative means to make transacting online as safe as possible. Artificial intelligence provides viable and efficient solutions via Machine Learning models/algorithms. In this project, I trained a fraud detection model to predict online payment fraud using Blossom Bank PLC as case study. Blosssom Bank ( BB PLC) is a multinational financial services group, that offers retail and investment banking, pension management, assets management and payment services, headquartered in London, UK. Blossom Bank wants to build a machine learning model to predict online payment fraud. Here is the dataset used for this task. With this model, BB PLC will: Keep up with fast evolving technological threats and better prevent the loss of funds (profit) to fraudsters. Accurately detect and identify anomalies in managing online transactions done on its platforms which may go undetected using traditional rules-based methods. 3.Improve quality assurance thus retaining old customers and acquire new ones. This will increase credit/profit base. Improve its policy and decision making. Steps: 1.Loading necessary python libraries. Loading Dataset. Exploratory Data Analysis. Higlighting Relationships and insights. Data Transformation; Using resampling techniques to address Class-imbalace.. Feature Engineering. Model Training. Model Evaluation. Challenges: I encountered a number of challenges during coding which made me run into error reports. these were due to improper documentations, syntax, especially during feature engineering (one-hot encoding: 'fit.transform'). This aspect consumed most of my time I was able to solve these challenges by making extensive research and paying close attention to syntax. I was able to selve the encoding by using 'pd.get_dummies() and making some specifications in the methods.
vickysort/Exploratory-Data-Analysis
Exploratory Data Analysis for a bank to mitigate Financial Risk of Charged-Off loans
WillKoehrsen/wikipedia-data-science
Working with and analyzing Wikipedia Data
WillKoehrsen/Data-Analysis
Data Science Using Python
rhiever/Data-Analysis-and-Machine-Learning-Projects
Repository of teaching materials, code, and data for my data analysis and machine learning projects.
Snowflake-Labs/sfguide-recommender-pipeline
Snowflake Guide: Building a Recommendation Engine Using Snowflake & Amazon SageMaker
aws-samples/amazon-kinesis-sagemaker-promotion-recommendations
CloudFormation templates and scripts demonstrating how to build a promotion recommendation system using Kinesis and SageMaker.
scikit-learn/scikit-learn
scikit-learn: machine learning in Python