Predictive Analysis 预测分析 (MAV551)

MAV551 Predictive Analysis provides students with the skills to develop and evaluate predictive models for business decision-making using Python. The course covers essential concepts such as selecting an analytical methodology, linear regression, classification, and model evaluation. Students will gain hands-on experience in data preprocessing, feature engineering, and applying machine learning techniques using Python libraries like scikit-learn and pandas. By the end of the course, they will be able to build reproducible predictive workflows, select appropriate models, and effectively communicate data-driven insights for management decision-making. MAV551预测分析旨在使学生掌握使用Python开发和评估业务决策预测模型的技能。课程涵盖选择分析方法、线性回归、分类和模型评估等核心概念。学生将通过Python库（如scikit-learn和pandas）学习数据预处理、特征工程和机器学习技术的实操。课程结束时，他们将能够构建可重复的预测工作流程，选择合适的模型，并有效地将数据驱动的洞察传达给管理层用于决策。

• Introduction to Predictive Analysis and its Applications预测分析及其应用简介
• Selecting an Analytical Methodology for Predictive Modelling预测建模的分析方法选择
• Introduction to Regression Analysis and Its Use Cases回归分析及其应用案例
• Building Linear Regression Models with Python使用Python构建线性回归模型
• Evaluating Regression Model Performance and Metrics评估回归模型的性能与指标
• Introduction to Classification Analysis and Its Applications分类分析及其应用简介
• Building Classification Models (e.g., Logistic Regression, Decision Trees) with Python使用Python构建分类模型（例如逻辑回归、决策树）
• Evaluating Classification Models Using Accuracy, Precision, Recall, and F1-Score使用准确率、精确率、召回率和F1-分数评估分类模型
• Handling Imbalanced Datasets in Predictive Modelling处理预测建模中的不平衡数据集
• Feature Engineering Techniques for Predictive Models预测模型的特征工程技术
• Introduction to Model Validation Techniques (e.g., Cross-Validation, Train-Test Split) 模型验证技术简介（例如交叉验证、训练-测试分割）
• Implementing Reproducible Workflows for Predictive Analysis using Python使用Python实现预测分析的可重复工作流程

• Assess appropriate predictive analytics techniques for various business problems评估适用于各种业务问题的预测分析技术
• Critique the strengths and limitations of various predictive models and methodologies批判性地评价各种预测模型和方法的优点和局限性
• Design applications of regression and classification models for solving real-world business challenges设计有效的回归和分类模型解决实际业务问题
• Construct Python programs to develop predictive models using regression and classification techniques构建Python程序，使用回归和分类技术开发预测模型
• Design and implement end-to-end predictive workflows, from data preprocessing to model evaluation, using Python libraries 使用Python库设计和实施从数据预处理到模型评估的端到端预测工作流程
• Create reproducible and scalable predictive analytics solutions tailored to business decision-making needs创建可重复且可扩展的预测分析解决方案，以满足业务决策需求