本项目旨在通过使用机器学习技术预测客户个性来提高营销活动的有效性。通过了解客户个性特征，企业可以调整其营销策略，以更好地满足目标受众的需求和偏好。
This project aims to enhance marketing campaign effectiveness by predicting customer personalities using machine learning techniques. By understanding customer personality traits, businesses can tailor their marketing strategies to better meet the needs and preferences of their target audience. (2024-07-25, Jupyter Notebook, 0KB, 下载0次)
该项目侧重于通过数据挖掘和Python编程来有效地分类客户端个性的可用数据量。通过采用先进的聚类技术，该项目试图揭示客户偏好和行为的模式，使企业能够更准确地定制其营销工作。
The project focuses on the amount of data available through data mining and Python programming to categorize client personalities effectively. By employing advanced clustering techniques, the project seeks to uncover patterns in customer preferences and behaviors, allowing businesses to tailor their marketing efforts more precisely. (2024-06-03, Jupyter Notebook, 0KB, 下载0次)
客户市场细分是一项关键任务，有助于企业了解其客户的多样性，并更准确地满足其产品需求。通过实现无监督学习，特别是K-Means等聚类算法，我们旨在发现客户数据中的隐藏模式和分组，而无需事先标记数据。
Customer market segmentation is a critical task that helps businesses understand the diversity of their customers and cater their offerings more precisely. By implementing unsupervised learning, particularly clustering algorithms like K-Means, we aim to discover hidden patterns and groupings in the customer data without prior labeling of the data. (2024-06-02, Jupyter Notebook, 0KB, 下载0次)
使用人工智能提供高级客户细分的项目。使用K-modes算法，根据行为、人口统计学和习惯对客户进行分组。这些细分市场有助于企业优化其营销战略。
A project providing advanced customer segmentation using artificial intelligence. Customers are grouped based on behavior, demographics, and habits using the K-modes algorithm. These segments assist businesses in optimizing their marketing strategies. (2024-03-18, Jupyter Notebook, 0KB, 下载0次)
使用从公司内部数据库中提取的一系列新闻文章，并根据其内容将其分为政治、技术、体育、商业和娱乐等几个类别。使用自然语言处理，创建并比较至少三个不同的模型。
Use a bunch of news articles extracted from the companies’ internal database and categorize them into several categories like politics, technology, sports, business and entertainment based on their content. Use natural language processing and create & compare at least three different models. (2024-03-06, Jupyter Notebook, 0KB, 下载0次)
应用K-means聚类进行客户市场细分涉及对具有相似购买行为的个人进行分类，使企业能够定制有针对性的营销策略。这种方法通过识别和理解不同客户群体之间的共同特征来提高外联工作的准确性。
Applying K-means clustering for customer market segmentation involves categorizing individuals with similar purchasing behaviors, enabling businesses to customize targeted marketing strategies. This approach enhances precision in outreach efforts by identifying and understanding shared characteristics among distinct customer segments. (2024-02-01, Jupyter Notebook, 0KB, 下载0次)
利用K-Means聚类进行深入的客户细分，使企业能够根据特定的客户类型定制产品。
Leveraging K-Means clustering for insightful customer segmentation, enabling businesses to tailor products to specific customer types. (2024-01-18, Jupyter Notebook, 0KB, 下载0次)
分类文本是否与政治、娱乐、体育、商业和技术相关
Classify the Text Whether It is Related to Politics,Entertainment,Sports,Business and Technology (2024-01-01, Jupyter Notebook, 0KB, 下载0次)
零售企业忠诚度成员中最可能的购买者的logistic回归模型
a logistic regression model to target the most probable buyers amongst loyalty members of a retail company (2023-12-31, Jupyter Notebook, 0KB, 下载0次)
TAO系统的公共存储库，结合基于OWL的本体技术和时间聚类方法来解释语义...，
Public repository for the TAO system that combines OWL-based ontological techniques and temporal clustering methods to interpret semantically meaningful high-level contexts from low-level human activity patterns. (2023-09-07, Jupyter Notebook, 0KB, 下载0次)
基于模糊聚类和深度神经网络的捷信违约风险评估，
Home Credit Default Risk Assessment using Fuzzy Clustering and Deep Neural Networks, (2021-01-01, Jupyter Notebook, 0KB, 下载0次)
企业集群分析，
Cluster Analysis for Business, (2023-08-28, Jupyter Notebook, 0KB, 下载0次)
客户细分是一种强大的技术，企业使用它来更好地了解他们的客户并定制他们的营销战略...，
Customer segmentation is a powerful technique used by businesses to understand their customers better and tailor their marketing strategies accordingly. In this project, we aim to apply the K-means clustering algorithm to segment customers based on their similarities and differences in purchasing behavior, preferences, and characteristics. (2023-08-07, Jupyter Notebook, 0KB, 下载0次)
确定了使用Random Forest为小型企业预测用户采用的因素
Identified the factors that predict user adoption using Random Forest for a small business (2018-05-20, Jupyter Notebook, 1184KB, 下载0次)
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.
The random forest algorithm is used to predict the enterprise rating. The random forest algorithm is used to predict the enterprise rating (2020-09-13, Jupyter Notebook, 1448KB, 下载0次)
低信噪比下的鲁棒自动调制分类，，
Robust-Automatic-Modulation-Classification-in-Low-Signal-to-Noise-Ratio,, (2023-02-19, Jupyter Notebook, 7000KB, 下载0次)
K-Means聚类-企业可以针对特定的客户群体
K-Means Clustering - Business can target specific groups of customers (2021-01-16, Jupyter Notebook, 6KB, 下载0次)
实践代码|阅读我的博客@[https:media.com@thepasadpatil k-means-clustering-inidentifying-f-r-i-e-n.d..]（https:media.com.cn@thepasadapatil k-means-clustering-Inidentifying-fo-r-i-e-nd-s-in-the-world-of-signgers-695537505d）
Hands on practice code | read my blog @ [https: medium.com @theprasadpatil k-means-clustering-identifying-f-r-i-e- n-d…](https: medium.com @theprasadpatil k-means-clustering-identifying-f-r-i- e-n-d-s-in-the-world-of-strangers-695537505d) (2022-01-19, Jupyter Notebook, 484KB, 下载0次)
巴西电子商务企业Olist的数据分析
Data analysis about Brazilian e-commerce business Olist (2022-01-30, Jupyter Notebook, 64682KB, 下载0次)
使用KMeans对小企业创新研究（SBIR）数据的TF IDF特征进行文档聚类
Document clustering using KMeans on TF IDF features on Small Business Innovation Research (SBIR) data (2021-03-17, Jupyter Notebook, 2483KB, 下载0次)