在本项目中，比较了三种学习模型：ARIMA（自回归综合移动平均）、随机森林回归（RFR）和长短期记忆神经网络（LSTM-NN）。使用了从博亚卡省757个不同地理点收集的月降雨量数据，单位为毫米（mm）。
In this project, comparisons were made between three learning models: ARIMA (Autoregressive Integrated Moving Average), Random Forest Regression (RFR), and Long Short-Term Memory Neural Network (LSTM-NN). Monthly precipitation data in millimetres (mm) collected from 757 different geographical points in the Boyacá department were used. (2024-04-15, Jupyter Notebook, 0KB, 下载0次)
该存储库包含一个机器学习项目，专注于预测泰坦尼克号上乘客的生存。该项目使用sklearn库中的支持向量回归（SVR）模型，涉及数据预处理和预测。
This repository contains a machine learning project focused on predicting the survival of passengers on the Titanic. The project uses a Support Vector Regression (SVR) model from the sklearn library and involves data preprocessing and prediction. (2024-01-07, Jupyter Notebook, 0KB, 下载0次)
该项目使用情感分析（VADER）预测酒店评论得分。情感分析的极性得分作为特征，而评论者得分是目标。集成回归模型，特别是随机森林，因其处理过拟合和组合多棵树以进行准确预测的能力而表现出色。
This project predicts hotel review scores using sentiment analysis (VADER). Polarity scores from sentiment analysis serve as features, while reviewer scores are the target. Ensemble regression models, particularly Random Forest, excel due to their ability to handle overfitting and combine multiple trees for accurate predictions. (2023-12-26, Jupyter Notebook, 0KB, 下载0次)