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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-credit-card-fraud-detection-model-building/
课程评论：没有评论
**课程名称：** 数据科学：信用卡欺诈检测 - 模型构建 **课程概要：** 本课程将深入教授如何利用多种机器学习模型，构建高精度信用卡欺诈检测模型，以准确区分欺诈交易和合法交易。这是一个实践性项目，将一步步指导您完成机器学习模型的创建与评估过程。 课程内容涵盖： * **数据探索与理解：** 从数据的初始探索和理解开始。 * **数据分析：** 进行深入的数据分析。 * **数据准备：** 包括特征工程等数据预处理步骤。 * **模型构建：** 学习和应用多种机器学习算法。 * **模型评估：** 掌握模型评估的各种指标和技术。 **核心技术与概念：** * **数据处理：** 学习安装和导入必要的Python库（如NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, Imbalanced-learn等）。 * **数据分析与可视化：** 加载、理解数据，检查目标变量的类别分布，分析特征间的相关性并绘制热力图，可视化变量分布。 * **模型构建基础：** 掌握 `train_test_split` 的应用。 * **模型评估指标：** 理解混淆矩阵（Confusion Matrix）、分类报告（Classification Report）和AUC-ROC曲线。 * **基础及常用机器学习模型：** 学习并实现逻辑回归 (Logistic Regression)、K近邻 (KNN)、决策树 (Tree)、随机森林 (Random Forest)、XGBoost 和支持向量机 (SVM) 等算法，并创建通用的函数来进行模型的训练和预测。 * **交叉验证：** 深入理解 `RepeatedKFold` 和 `StratifiedKFold`，并将其应用于模型评估。 * **处理类别不平衡问题：** 学习并实践 `Random Oversampler`、`SMOTE` 和 `ADASYN` 等过采样技术，以应对欺诈检测场景中常见的类别不平衡问题。 * **模型优化：** 进行超参数调优（Hyperparameter Tuning）以选出最优模型。 * **特征重要性：** 学习提取最关键的特征。 * **最终推断：** 对整个建模过程进行总结和最终推断。 **课程收益：** * 获取 AutomationGig 颁发的结业证书。 * 课程提供的所有数据集。 * 课程结束时提供的Jupyter Notebook及相关项目文件。 **课程目标：** 掌握现代数据分析和模型构建中最具需求的技能之一，在短时间内大幅提升机器学习能力。 **立即报名，开启您的21世纪热门技能学习之旅！**
In this course I will cover, how to develop a Credit Card Fraud Detection model to categorize a transaction as Fraud or Legitimate with very high accuracy using different Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model.This course will walk you through the initial data exploration and understanding, data analysis, data preparation, model building and evaluation. We will explore RepeatedKFold, StratifiedKFold, Random Oversampler, SMOTE, ADASYN concepts and then use multiple ML algorithms to create our model and finally focus into one which performs the best on the given dataset.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1 : Installing Packages.Task 2 : Importing Libraries.Task 3 : Loading the data from source.Task 4 : Understanding the dataTask 5 : Checking the class distribution of the target variableTask 6 : Finding correlation and plotting Heat MapTask 7 : Performing Feature engineering.Task 8 : Train Test SplitTask 9: Plotting the distribution of a variableTask 10: About Confusion Matrix, Classification Report, AUC-ROCTask 11: Created a common function to plot confusion matrixTask 12: About Logistic Regression, KNN, Tree, Random Forest, XGBoost, SVM modelsTask 13: Created a common function to fit and predict on a Logistic Regression modelTask 14: Created a common function to fit and predict on a KNN modelTask 15: Created a common function to fit and predict on a Tree modelsTask 16: Created a common function to fit and predict on a Random Forest modelTask 17: Created a common function to fit and predict on a XGBoost modelTask 18: Created a common function to fit and predict on a SVM modelTask 19: About RepeatedKFold and StratifiedKFold.Task 20: Performing cross validation with RepeatedKFold and Model EvaluationTask 21: Performing cross validation with StratifiedKFold and Model EvaluationTask 22: Proceeding with the model which shows the best result till nowTask 23: About Random Oversampler, SMOTE, ADASYN.Task 24: Performing oversampling with Random Oversampler with StratifiedKFold cross validation and Model Evaluation.Task 25: Performing oversampling with SMOTE and Model Evaluation.Task 26: Performing oversampling with ADASYN and Model Evaluation.Task 27: Hyperparameter Tuning.Task 28: Extracting most important featuresTask 29: Final Inference.Data Analysis, Model Building is one of the most demanded skill of the 21st century. Take the course now, and have a much stronger grasp of Machine learning in just a few hours!You will receive:1. Certificate of completion from AutomationGig.2. All the datasets used in the course are in the resources section.3. The Jupyter notebook and other project files are provided at the end of the course in the resource section.So what are you waiting for?Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We'll see you inside the course!Happy Learning!![Please note that this course and its related contents are for educational purpose only][Music: bensound]
