本案例对信用卡欺诈数据进行描述性统计分析，主要是对没有进行主成分分析转化的三个属性，从定性角度分析信用卡违约的影响因素。然后对数据进行建模分析，主要使用逻辑回归、KNN，支持向量机，决策树等机器学习的分类算法，同时不断进行参数的最优选择，最终得到分类效果准确且有效模型。本案例的数据高度不平衡，因此需要结合不平衡数据处理方法，对上述的最优模型不断优化改进，得到分类效果更为有效的分类器。通过一系列的分析建模，最终对信用卡欺诈得到有效分类，进而使银行减少信用风险导致的损失。
"
["minPrice"]=>
string(4) "0.00"
["maxPrice"]=>
string(4) "0.00"
["discountId"]=>
string(1) "0"
["images"]=>
array(3) {
["large"]=>
string(76) "http://www.chinadatacase.com/files/course/2023/05-05/144951ff3cb4048068.jpeg"
["middle"]=>
string(76) "http://www.chinadatacase.com/files/course/2023/05-05/1449520002aa749326.jpeg"
["small"]=>
string(76) "http://www.chinadatacase.com/files/course/2023/05-05/1449520008f9685852.jpeg"
}
["ratingNum"]=>
string(1) "0"
["rating"]=>
string(1) "0"
["hitNum"]=>
string(3) "616"
["hotSeq"]=>
string(1) "0"
["maxPriceObj"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["minPriceObj"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["minDisplayPrice"]=>
string(4) "0.00"
["maxDisplayPrice"]=>
string(4) "0.00"
["minDisplayPriceObj"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["maxDisplayPriceObj"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["canManage"]=>
bool(false)
["peopleShowNum"]=>
string(3) "131"
["isMember"]=>
bool(false)
["status"]=>
string(9) "published"
["orgId"]=>
string(1) "1"
["orgCode"]=>
string(2) "1."
["recommendWeight"]=>
string(1) "0"
["recommendedTime"]=>
string(1) "0"
["createdTime"]=>
string(25) "2022-03-02T16:09:06+08:00"
["updatedTime"]=>
string(25) "2025-12-10T03:53:30+08:00"
["product"]=>
array(7) {
["id"]=>
string(3) "147"
["targetType"]=>
string(6) "course"
["title"]=>
string(78) "金融风控？！基于机器学习算法的信用卡欺诈模型构建研究"
["owner"]=>
string(1) "8"
["createdTime"]=>
string(10) "1646208546"
["updatedTime"]=>
string(10) "1697079888"
["target"]=>
array(17) {
["id"]=>
string(3) "181"
["type"]=>
string(6) "normal"
["title"]=>
string(78) "金融风控？！基于机器学习算法的信用卡欺诈模型构建研究"
["subtitle"]=>
string(0) ""
["summary"]=>
string(940) "本案例对信用卡欺诈数据进行描述性统计分析，主要是对没有进行主成分分析转化的三个属性，从定性角度分析信用卡违约的影响因素。然后对数据进行建模分析，主要使用逻辑回归、KNN，支持向量机，决策树等机器学习的分类算法，同时不断进行参数的最优选择，最终得到分类效果准确且有效模型。本案例的数据高度不平衡，因此需要结合不平衡数据处理方法，对上述的最优模型不断优化改进，得到分类效果更为有效的分类器。通过一系列的分析建模，最终对信用卡欺诈得到有效分类，进而使银行减少信用风险导致的损失。
"
["cover"]=>
array(3) {
["large"]=>
string(76) "http://www.chinadatacase.com/files/course/2023/05-05/144951ff3cb4048068.jpeg"
["middle"]=>
string(76) "http://www.chinadatacase.com/files/course/2023/05-05/1449520002aa749326.jpeg"
["small"]=>
string(76) "http://www.chinadatacase.com/files/course/2023/05-05/1449520008f9685852.jpeg"
}
["status"]=>
string(9) "published"
["studentNum"]=>
string(3) "131"
["discountType"]=>
string(8) "discount"
["discount"]=>
string(2) "10"
["minCoursePrice"]=>
string(4) "0.00"
["maxCoursePrice"]=>
string(4) "0.00"
["defaultCourseId"]=>
string(3) "181"
["productId"]=>
string(3) "147"
["goodsId"]=>
string(3) "147"
["minCoursePrice2"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["maxCoursePrice2"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
}
}
["extensions"]=>
array(3) {
[0]=>
string(8) "teachers"
[1]=>
string(14) "recommendGoods"
[2]=>
string(10) "isFavorite"
}
["specs"]=>
array(1) {
[0]=>
array(26) {
["id"]=>
string(3) "147"
["goodsId"]=>
string(3) "147"
["targetId"]=>
string(3) "181"
["title"]=>
string(0) ""
["seq"]=>
string(1) "1"
["status"]=>
string(9) "published"
["price"]=>
string(4) "0.00"
["coinPrice"]=>
string(4) "0.00"
["usageMode"]=>
string(7) "forever"
["usageDays"]=>
string(1) "0"
["usageStartTime"]=>
string(1) "0"
["usageEndTime"]=>
string(1) "0"
["buyableStartTime"]=>
string(1) "0"
["buyableEndTime"]=>
string(1) "0"
["buyableMode"]=>
NULL
["buyable"]=>
string(1) "1"
["maxJoinNum"]=>
string(1) "0"
["services"]=>
array(0) {
}
["priceObj"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["displayPrice"]=>
string(4) "0.00"
["displayPriceObj"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["isMember"]=>
bool(false)
["access"]=>
array(2) {
["code"]=>
string(14) "user.not_login"
["msg"]=>
string(15) "用户未登录"
}
["hasCertificate"]=>
bool(false)
["learnUrl"]=>
string(42) "http://www.chinadatacase.com/my/course/181"
["teachers"]=>
array(1) {
[0]=>
array(6) {
["id"]=>
string(3) "979"
["nickname"]=>
string(11) "lzy20051028"
["title"]=>
string(27) "金融数据挖掘工程师"
["uuid"]=>
string(40) "9b4f4a7c83e7b62eb169ded7a353482e634f7934"
["destroyed"]=>
string(1) "0"
["avatar"]=>
array(3) {
["small"]=>
string(73) "http://www.chinadatacase.com/files/user/2022/07-20/122655fae409010852.jpg"
["middle"]=>
string(73) "http://www.chinadatacase.com/files/user/2022/07-20/122655fadca0412458.jpg"
["large"]=>
string(73) "http://www.chinadatacase.com/files/user/2022/07-20/122655fad469539377.jpg"
}
}
}
}
}
}
{
"i18nChapterName": "章",
"i18nUnitName": "节",
"i18nLessonName": "课时",
"i18nTaskName":"任务"
}
{
"text": {
"icon": "es-icon es-icon-graphic",
"name": "图文"
}
,
"video": {
"icon": "es-icon es-icon-video",
"name": "视频"
}
,
"audio": {
"icon": "es-icon es-icon-audio",
"name": "音频"
}
,
"live": {
"icon": "es-icon es-icon-entry-live",
"name": "直播"
}
,
"discuss": {
"icon": "es-icon es-icon-discuss",
"name": "讨论"
}
,
"flash": {
"icon": "es-icon es-icon-flash",
"name": "Flash"
}
,
"doc": {
"icon": "es-icon es-icon-document",
"name": "文档"
}
,
"ppt": {
"icon": "es-icon es-icon-ppt",
"name": "PPT"
}
,
"testpaper": {
"icon": "es-icon es-icon-examination",
"name": "考试"
}
,
"homework": {
"icon": "es-icon es-icon-task",
"name": "作业"
}
,
"exercise": {
"icon": "es-icon es-icon-exercise",
"name": "练习"
}
,
"download": {
"icon": "es-icon es-icon-downloadfile",
"name": "下载资料"
}
,
"cases": {
"icon": "es-icon es-icon-school",
"name": "实验"
}
}
1765636907
