feat: 初始化员工缺勤分析系统项目

搭建完整的前后端分离架构，实现数据概览、预测分析、聚类分析等核心功能模块

  详细版：
  feat: 初始化员工缺勤分析系统项目

  - 后端：基于 Flask 搭建 RESTful API，包含数据概览、特征分析、预测模型、聚类分析四大模块
  - 前端：基于 Vue.js 构建单页应用，实现 Dashboard、预测、聚类、因子分析等页面
  - 模型：集成随机森林、XGBoost、LightGBM、Stacking 等多种机器学习模型
  - 文档：完成需求规格说明、系统架构设计、接口设计、数据设计、UI原型设计等文档

backend/config.py

@@ -0,0 +1,148 @@
+import os
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+DATA_DIR = os.path.join(BASE_DIR, 'data')
+RAW_DATA_DIR = os.path.join(DATA_DIR, 'raw')
+PROCESSED_DATA_DIR = os.path.join(DATA_DIR, 'processed')
+
+MODELS_DIR = os.path.join(BASE_DIR, 'models')
+
+RAW_DATA_PATH = os.path.join(RAW_DATA_DIR, 'Absenteeism_at_work.csv')
+CLEAN_DATA_PATH = os.path.join(PROCESSED_DATA_DIR, 'clean_data.csv')
+
+RF_MODEL_PATH = os.path.join(MODELS_DIR, 'rf_model.pkl')
+XGB_MODEL_PATH = os.path.join(MODELS_DIR, 'xgb_model.pkl')
+KMEANS_MODEL_PATH = os.path.join(MODELS_DIR, 'kmeans_model.pkl')
+SCALER_PATH = os.path.join(MODELS_DIR, 'scaler.pkl')
+ENCODER_PATH = os.path.join(MODELS_DIR, 'encoder.pkl')
+
+CSV_SEPARATOR = ';'
+
+RANDOM_STATE = 42
+TEST_SIZE = 0.2
+
+FEATURE_NAMES = [
+    'ID',
+    'Reason for absence',
+    'Month of absence',
+    'Day of the week',
+    'Seasons',
+    'Transportation expense',
+    'Distance from Residence to Work',
+    'Service time',
+    'Age',
+    'Work load Average/day ',
+    'Hit target',
+    'Disciplinary failure',
+    'Education',
+    'Son',
+    'Social drinker',
+    'Social smoker',
+    'Pet',
+    'Weight',
+    'Height',
+    'Body mass index',
+    'Absenteeism time in hours'
+]
+
+CATEGORICAL_FEATURES = [
+    'Reason for absence',
+    'Month of absence',
+    'Day of the week',
+    'Seasons',
+    'Disciplinary failure',
+    'Education',
+    'Social drinker',
+    'Social smoker'
+]
+
+NUMERICAL_FEATURES = [
+    'Transportation expense',
+    'Distance from Residence to Work',
+    'Service time',
+    'Age',
+    'Work load Average/day ',
+    'Hit target',
+    'Son',
+    'Pet',
+    'Body mass index'
+]
+
+REASON_NAMES = {
+    0: '未知原因',
+    1: '传染病',
+    2: '肿瘤',
+    3: '血液疾病',
+    4: '内分泌疾病',
+    5: '精神行为障碍',
+    6: '神经系统疾病',
+    7: '眼部疾病',
+    8: '耳部疾病',
+    9: '循环系统疾病',
+    10: '呼吸系统疾病',
+    11: '消化系统疾病',
+    12: '皮肤疾病',
+    13: '肌肉骨骼疾病',
+    14: '泌尿生殖疾病',
+    15: '妊娠相关',
+    16: '围产期疾病',
+    17: '先天性畸形',
+    18: '症状体征',
+    19: '损伤中毒',
+    20: '外部原因',
+    21: '健康因素',
+    22: '医疗随访',
+    23: '医疗咨询',
+    24: '献血',
+    25: '实验室检查',
+    26: '无故缺勤',
+    27: '理疗',
+    28: '牙科咨询'
+}
+
+WEEKDAY_NAMES = {
+    2: '周一',
+    3: '周二',
+    4: '周三',
+    5: '周四',
+    6: '周五'
+}
+
+SEASON_NAMES = {
+    1: '夏季',
+    2: '秋季',
+    3: '冬季',
+    4: '春季'
+}
+
+EDUCATION_NAMES = {
+    1: '高中',
+    2: '本科',
+    3: '研究生',
+    4: '博士'
+}
+
+FEATURE_NAME_CN = {
+    'ID': '员工标识',
+    'Reason for absence': '缺勤原因',
+    'Month of absence': '缺勤月份',
+    'Day of the week': '星期几',
+    'Seasons': '季节',
+    'Transportation expense': '交通费用',
+    'Distance from Residence to Work': '通勤距离',
+    'Service time': '工龄',
+    'Age': '年龄',
+    'Work load Average/day ': '日均工作负荷',
+    'Hit target': '达标率',
+    'Disciplinary failure': '违纪记录',
+    'Education': '学历',
+    'Son': '子女数量',
+    'Social drinker': '饮酒习惯',
+    'Social smoker': '吸烟习惯',
+    'Pet': '宠物数量',
+    'Weight': '体重',
+    'Height': '身高',
+    'Body mass index': 'BMI指数',
+    'Absenteeism time in hours': '缺勤时长'
+}