Pangyu's repositories
multi-factor-strategy-joinquant
在聚宽（joinquant）平台上使用多因子策略进行量化投资模拟。
Air-Quality-Prediction
2021年研究生数学建模竞赛B题，全国二等奖，空气质量预报二次建模，时间序列数据分析与回归预测。Time Series Prediction&Air Quality Prediction.
Chinese-Sentiment-Analysis-and-LDA-Topic
使用中文情感词汇本体库进行情感分析，之后对每种情感的文本进行主题分析。Using Chinese Sentiment Dictionary for Sensitive Analysis, Then applying LDA Topic Analysis for each Emotion.
Random-Forest-Parameter-Selection
通过十折交叉验证进行参数选择，最后利用最优参数进行随机森林回归预测。Through ten fold cross validation, the parameters were selected, and finally the optimal parameters were used for random forest regression prediction.
GMM-KMeans-for-Outlier-Detection
针对一维时间序列数据，采用GMM和K-Means算法进行异常点检测。For one-dimensional time series data, GMM and K-means algorithm are used to detect outliers.
CVPR-2020-LEAP
Unofficial implement of LEAP(Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective) for Multi-Label Classification.
Multi-LSTM-for-Regression
使用LSTM处理回归问题，每个输入特征的时间窗维度不一样，因此，也可以看作利用了多个LSTM特征提取器。When LSTM is used to deal with regression problems, the time window dimension of each input feature is different. Therefore, it can also be regarded as using multiple LSTM feature extractors.
LDA-for-Chinese-Topic-Generation
使用LDA模型进行中文文本的主题生成。Using LDA Model for Chinese Topic Generation.
P300-BCI-Data-Analysis
2020年研究生数学建模竞赛C题，全国二等奖，分析脑机接口数据进行分析预测。The data of BCI were analyzed and predicted.
ARIMA-Plot-of-Residuals
使用AIC准则进行参数选择，之后采用ARIMA模型进行时间序列预测，最后给出残差图。The AIC criterion is used to select the parameters, and then ARIMA model is used to predict the time series. Finally, the residual diagram is given.
NCF-MF-for-Recommendation
分别使用传统方法（KNN，SVD，NMF等）和深度方法（NCF）进行推荐系统的评分预测。Traditional methods (KNN, SVD, NMF, etc.) and depth method (NCF) were used to predict rating of the recommendation system.
Credit-Data-Analysis
实现对信贷数据的数据预处理，数据分析。之后利用多种分类算法对公司是否违约进行预测。Realize the data preprocessing and data analysis of credit data. Then, it uses a variety of classification algorithms to predict whether the company defaults.
sklearn-regression-algorithm
常见sklearn回归算法（随机森林，adaboost，bagging，knn等）在示例数据集上的使用。The application of common sklearn regression algorithms (random forest, AdaBoost, bagging, KNN, etc.) on the sample dataset.
MF-for-Movie-Recommendation
使用矩阵分解方法进行电影推荐的评分预测。The matrix factorization method is used to predict the rating of movie recommendation.
Yelp-Recomendation-Algorithms
在Yelp数据集上摘取部分评分数据进行多种推荐算法（SVD,SVDPP,PMF,NMF）的性能对比。Some rating data are extracted from yelp dataset to compare the performance of various recommendation algorithms(SVD,SVDPP,PMF,NMF).
Comment-Sentiment-Analysis
使用基于情感词典的情感分析方法对评论信息进行情感分析。The Sentiment Analysis Method based on Sentiment Dictionary is used for Comment Information.
LDA-gensim
使用LDA模型提取n个句子的主题，并统计每个主题出现的频次。LDA model is used to extract the topic of N sentences, and the frequency of each topic is counted.
Matrix-Factorization-Implicit-Feedback
使用矩阵分解算法处理隐式反馈数据，并进行Top-N推荐。The matrix factorization algorithm is used to process the implicit feedback data and make top-N recommendation.
NCF-for-Implicit-Feedback
Neural collaborative filtering (NCF) method is used for Microsoft MIND news recommendation dataset.
Random-Forest-Regression
使用随机森林算法对企业评级进行预测。The random forest algorithm is used to predict the enterprise rating.
TF-IDF-for-Chinese-Keywords-Generation
使用TF-IDF算法进行中文关键词生成任务。Using TF-IDF Algorithm to Generate Chinese Keywords.
Multi-Label-Classification-Data-Preprocessing
对于多标签分类数据集的预处理。Data preprocessing for multi label classification.
Pension-industry
MathorCup，2020，B题，数学建模代码