telco_churn_anonymized_dataset_italy

This dataset supports the research presented in the paper:"[FILL IN: Article Title]", submitted to [FILL IN: Journal]. Overview--------An anonymized, semester-aggregated dataset of customer records from a real-worldItalian telecommunications company, curated for churn prediction research.The dataset covers multiple semesters and contains over 16 million records acrossapproximately 4.5 million unique customers. The primary prediction target is will_churn: a binary variable indicating whethera customer discontinued their service in the semester following the observation period. Dataset Structure-----------------The data is organized by semester (e.g., '2023_0', '2023_1', '2024_0', '2024_1').Due to upload size constraints, the dataset is split into four files, one per semester.To use with the provided training scripts, merge them into a single CSV namedtelco-dataset-anonim.csv, or modify the scripts to load files individually. Key Features------------- wifi_offer_name: Wi-Fi subscription plan name (categorical, anonymized)- wifi_fee_sem_k: Wi-Fi revenue for the semester (thousands of EUR)- mobile_fee_sem_k: Mobile revenue for the semester (thousands of EUR)- n_mobile_offers_sem: Number of active mobile subscriptions in the semester- payment_method: Customer payment method (categorical, anonymized)- tenure_norm: Normalized customer tenure- user_engagement_score: Composite engagement metric- total_pendings_sem_k: Total pending payments for the semester (thousands of EUR)- age_norm: Normalized customer age- cap_extracted: Postal code area (anonymized)- will_churn: Binary target variable (1 = churned in next semester, 0 = retained)- customer_code: Anonymized customer identifier (SHA-256 hash)- semester: Observation period label Anonymization-------------All direct and indirect personal identifiers have been anonymized in compliancewith GDPR. Customer codes were transformed using SHA-256 cryptographic hashing.Categorical variables containing potentially identifying information weregeneralized or encoded. No personally identifiable information (PII) or rawbilling details are present in this dataset. Pre-processing--------------Missing values in categorical columns were imputed with the string "Missing".The 'vat' field (VAT registration status) was treated as a boolean string.Numerical features were normalized. The dataset is ready for direct use withthe companion code repository. Companion Code--------------https://github.com/franc96/telco-churn-prediction-ml License-------Creative Commons Attribution 4.0 International (CC BY 4.0)本数据集用于支撑提交至[待填充：期刊名称]的论文"[待填充：文章标题]"中的相关研究。 概述--------本数据集为匿名化处理后的真实意大利电信企业客户记录集合，按半学年聚合，专为客户流失预测研究精心整理。数据集覆盖多个半学年周期，包含约450万唯一客户的超1600万条记录。 主要预测目标为will_churn：这一二元变量用于标识客户是否在观测周期后的半学年内终止了服务。 数据集结构-----------------数据按半学年组织（例如：'2023_0'、'2023_1'、'2024_0'、'2024_1'）。受限于上传文件大小限制，本数据集被拆分为4个文件，每个半学年对应一个文件。若要使用配套训练脚本，需将这些文件合并为一个名为telco-dataset-anonim.csv的CSV文件，或修改脚本以单独加载各文件。 关键特征------------- - wifi_offer_name：Wi-Fi订阅套餐名称（分类变量，已匿名化） - wifi_fee_sem_k：半学年Wi-Fi营收（单位：千欧元） - mobile_fee_sem_k：半学年移动业务营收（单位：千欧元） - n_mobile_offers_sem：半学年内活跃移动订阅数 - payment_method：客户支付方式（分类变量，已匿名化） - tenure_norm：归一化后的客户在网时长 - user_engagement_score：用户综合参与度指标 - total_pendings_sem_k：半学年待缴总款项（单位：千欧元） - age_norm：归一化后的客户年龄 - cap_extracted：匿名化处理后的邮政编码区域 - will_churn：二元目标变量（1=下一半学年发生客户流失，0=客户留存） - customer_code：匿名化客户标识符（SHA-256哈希） - semester：观测周期标签 匿名化处理-------------本数据集已对所有直接及间接个人标识符进行匿名化处理，符合通用数据保护条例（General Data Protection Regulation，GDPR）要求。客户代码通过SHA-256加密哈希进行转换；包含潜在识别信息的分类变量已进行泛化或编码处理。本数据集未包含任何个人可识别信息（Personally Identifiable Information，PII）或原始账单细节。 预处理-------------分类列中的缺失值以字符串"Missing"进行插补。'vat'字段（增值税注册状态）被作为布尔字符串处理。数值特征已完成归一化，本数据集可直接配合配套代码仓库使用。 配套代码--------------https://github.com/franc96/telco-churn-prediction-ml 许可证-------知识共享署名4.0国际许可协议（Creative Commons Attribution 4.0 International，CC BY 4.0）