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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.

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2

PSO-RBF-NN

使用粒子群算法优化的RBF神经网络进行预测。RBF neural network optimized by particle swarm optimization is used for prediction.
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3

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.
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4

multi-factor-strategy-joinquant

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5

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使用RBF、BP神经网络进行预测。RBF/BP neural network is used for prediction.
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6

Stochastic-Process-Ross-2nd-edition

Here is the exercise solution of stochastic process Ross 2nd Edition collected by the author. The answers are from the stochastic process courses of Umich, Columbia University and BJTU respectively. Due to the different assignments assigned by each teacher, the answers provided by each university are not complete, for your comprehensive reference.
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7

BPNN-MATLAB

使用bp神经网络预测电力负荷,使用小型数据集,通过一个简单的例子。Using BPNN to predict power load, using small data set, a simple example.
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8

LSTM-regression-and-classification

使用LSTM对股票价格进行回归预测,对股价涨跌进行分类预测。We use LSTM to forecast the stock price and classify the rise and fall of the stock price.
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9

time-series-analysis

使用经典的AR、MA、ARMA、ARIMA、ARCH、GARCH时间序列模型进行模型的检验和拟合。The classic AR, MA, ARMA, ARIMA, ARCH, GARCH time series models are used to test and predict the model.
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10

Grey-Model

使用灰色系统理论做负荷预测。Using Grey System Theory to Make Load Forecasting
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11

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.
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12

Econometrics-Example

计量经济学的实例分析包括多元回归分析,多重共线性,对数回归,虚拟变量分段线性回归,多项式拟合以及时间序列。The case analysis of econometrics includes multiple regression analysis, multicollinearity, logarithm regression, piecewise linear regression of dummy variable, polynomial fitting and time series.
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13

LSFA

The code for our paper "Label-Specific Feature Augmentation for Long-Tailed Multi-Label Text Classification”
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14

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.
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15

imbalanced-classification

根据60个特征,70万条数据预测5G用户,一个典型的不平衡二分类问题。According to 60 features, 700000 pieces of data predict 5G users, a typical imbalance problem.
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16

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.
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GMM-KMeans-for-Outlier-Detection

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18

LDA-for-Chinese-Topic-Generation

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20

CVPR-2020-LEAP

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21

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22

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23

NCF-MF-for-Recommendation

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24

Credit-Data-Analysis

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25

SARIMA

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26

MF-for-Movie-Recommendation

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27

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28

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29

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31

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32

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33

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35

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36

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37

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38

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39

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40

Bayesian-Computation-with-R-Solutions

Part of the solutions about 《Bayesian Computation with R》(Jim Albert)
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41

BPNN-R

Using back propagation neural network(BPNN) to forecasting the price. 使用R语言实现BP神经网络回归预测。
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43

XML-data-processing

处理多个微博上爬取的XML数据,转换为pandas.dataframe格式。Process XML data crawled from multiple microblogs and convert it to pandas.dataframe format.
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44

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1
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45

logistic-regression-variable-selection

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46

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.
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