巴州客服工单自动聚类项目, stars:0, update:2024-06-21 11:36:21 (2024-06-23, Jupyter Notebook, 0KB, 下载0次)
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监督学习-逻辑回归，决策树-INN酒店
Supervised Learning - Logistic Regression, Decision trees - INN Hotels (2024-03-02, Jupyter Notebook, 0KB, 下载0次)
基于NLP的酒店评论分类
NLP based Hotel Reviews Classification (2024-02-23, Jupyter Notebook, 0KB, 下载0次)
经典的机器学习算法（如KNN、ANN、SVM、RF）在平坦的图像数据上叠加和训练。叠加模型输出被集成为最终结果。采用先进的元学习集成方法，对叠加模型输出进行训练。使用了多个元学习器，包括MLP、SVM和KNN。达到98%的精度。
Classic machine learning algorithms like KNN, ANN, SVM, RF are stacked and trained on flattened image data. Stacked model outputs are ensembled for final results. Advanced ensemble method of Meta-Learned is used which is trained on stacked models outputs. Multiple meta-learners are used including MLP, SVM & KNN. 98% Accuracy is achieved. (2024-02-07, Jupyter Notebook, 0KB, 下载0次)
该GitHub存储库托管了一个预测分析案例研究，旨在预测酒店预订取消情况。它包括EDA、机器学习模型（KNN、决策树）和用于平衡类的SMOTE。它由代码、数据集和报告组成，是了解酒店预订管理中数据科学应用的资源。
This GitHub repository hosts a predictive analytics case study aimed at forecasting hotel booking cancellations. It includes EDA, machine learning models (KNN, Decision Trees), and SMOTE for balancing classes. Complete with code, datasets, and a report, it serves as a resource for understanding data science applications in hotel booking management. (2024-02-07, Jupyter Notebook, 0KB, 下载0次)
它是一种ML模型，其中使用knn算法来预测肿瘤是否恶性。在这个模型中，我比较了结果…
It is a ML model in which knn algorithm is used to predict whether a tumor is malignant or not .In this model i have compared the results… (2024-01-15, Jupyter Notebook, 0KB, 下载0次)
酒店预订数据分析。在本笔记本中，我介绍了数据准备、EDA和可视化，以及决策树预测模型...
Analysis of hotel booking data. In this notebook I go over data preparation, EDA and visualizations, and a decision tree predictive model to classify the likelihood of customers booking cancellations. (2023-12-17, Jupyter Notebook, 0KB, 下载0次)
使用具有Logistic回归、kNN、决策树、SVM和NN的酒店预订需求数据集进行酒店预订取消预测
Hotel booking cancellation prediction using Hotel Booking Demand Dataset with Logistic Regression, kNNs, Decision Tree, SVM and NN (2023-11-27, Jupyter Notebook, 0KB, 下载0次)
基于朴素贝叶斯算法的酒店预订预测
Hotel Reservations Predictions Using Naive Bayes Algoritm (2023-11-24, Jupyter Notebook, 0KB, 下载0次)
构建决策树和Logistic回归模型预测酒店取消
Constructed Decision Tree and Logistic Regression Models to predict hotel cancellations (2023-11-21, Jupyter Notebook, 0KB, 下载0次)
不完全数据下乘客机票价格预测的混合PSO-XGboost-KNN模型
A hybrid PSO-XGboost-KNN model to predict the price of the airline tickets for passengers on incomplete data (2023-11-04, Jupyter Notebook, 0KB, 下载0次)
建立了基于树的模型，酒店可以使用该模型来预测哪些预订可能被取消，并了解重要的市场...
Built tree-based models that can be used by the hotel to predict which bookings are likely to be canceled and know the important marketing policies accordingly. Evaluated and tuned decision tree and random forest models. A project done under the MIT Applied Data Science Program hosted by Great Learning. (2023-10-31, Jupyter Notebook, 0KB, 下载0次)
酒店评论数据的Logistic回归模型，
Logistic Regression Model on Hotel Review Data, (2023-10-19, Jupyter Notebook, 0KB, 下载0次)
该笔记本使用逻辑回归和决策树模型来确定哪些客户将取消其即将到来的酒店预订。，
This notebook used logistical regression and decision tree models to determine which customers will cancel their upcoming hotel bookings., (2023-08-11, Jupyter Notebook, 0KB, 下载0次)
基于决策树的酒店预订取消预测，，
Hotel-Booking-Cancellation-Prediction-Using-Decision-Tree,, (2023-08-05, Jupyter Notebook, 0KB, 下载0次)
布洛涅博伊斯集团有限公司的应用去分类部分附加信息...
Application de classification par apprentissage profond pour mon projet d intégration au Collège de Bois-de-Boulogne à la session d hiver 2020 (2021-12-23, Jupyter Notebook, 3085KB, 下载0次)
使用Python、R、线性回归、Logistic回归、功能引擎预测泰坦尼克号上乘客的存活率...
Predict survival of a passenger on the Titanic using Python, R, Linear Regression, Logistic Regression, Feature Engineering & Random Forests (2017-01-08, Jupyter Notebook, 727KB, 下载0次)
本项目的目标是建立一个决策树模型，该模型可以预测列车上乘客的存活率...
The objective of this project is to build a decision tree model that can predict the survival of passengers on the Titanic based on certain features. The goal is to achieve high accuracy in predicting whether a passenger survived or not. (2023-04-05, Jupyter Notebook, 245KB, 下载0次)
服务外包大赛19年选题14-运用文本相似度实现(证券)智能客服【恒生电子】,回笼觉国家队代码
Selected topic of the service outsourcing contest in 19 years 14- use text similarity to achieve (securities) intelligent customer service [Hang Seng Electronics], and recall the national team code (2019-04-29, Jupyter Notebook, 44091KB, 下载0次)
Facebook的一个比赛,预测一个人想入住哪个酒店,数据集是十万平方公里内预订酒店的客户的坐标和时间等,返回最有可能的地方的排名列表。使用KNN进行预测。
A contest on Facebook to predict which hotel a person wants to stay in. The data set is the coordinates and time of customers who book hotels within 100000 square kilometers, and returns the ranking list of the most likely places. Use KNN for prediction. (2018-12-31, Jupyter Notebook, 101KB, 下载0次)