|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/logistic-regression-with-r-studio/
课程评论：没有评论
课程名称：使用R进行预测建模的逻辑回归 课程概述： 欢迎参加“使用R进行预测建模的逻辑回归”课程！在本课程中，我们将深入探讨逻辑回归这一强大的统计技术，它是建模二元结果的重要工具。从对广告数据的分析到信贷风险的预测，学员将获得将逻辑回归应用于真实世界数据集的实践经验。准备 unlock 数据的预测潜力，提升您的分析技能！ 课程内容： 第1部分：介绍 本部分概述了逻辑回归，这是一种用于建模二元结果与一个或多个自变量之间关系的强大统计技术。 第2部分：广告数据集 探索与广告相关的数据集，包括数据预处理、特征缩放及建立逻辑回归模型以预测结果的主题。 第3部分：糖尿病数据集 分析糖尿病数据集，包括逻辑回归建模、降维技术、混淆矩阵解读、ROC曲线绘制和阈值设置。 第4部分：信贷风险 通过涉及贷款状态、申请人收入、贷款金额、贷款期限和信用历史的数据集来研究信贷风险。学员将学习如何拆分数据集以进行训练和评估。 在本课程中，学员将： - 深入了解逻辑回归，这种统计方法用于二元分类任务。 - 学习如何预处理和探索真实世界数据集，例如广告和糖尿病数据集，为逻辑回归分析做好准备。 - 探索各种技术，包括特征缩放、降维和模型拟合，以优化逻辑回归模型以获得准确预测。 - 理解如何使用混淆矩阵、ROC曲线和曲线下面积（AUC）等关键指标评估逻辑回归模型的性能。 - 将逻辑回归应用于实际场景，例如通过分析相关特征（如受抚养人、申请人收入、贷款金额、贷款期限和信用历史）进行信贷风险评估。 - 获取使用Python、pandas、scikit-learn和matplotlib等工具进行数据操作、模型建立和评估的实践经验。 总体而言，学员将发展在各个领域有效应用逻辑回归的技能和知识，基于二元结果做出数据驱动的决策和预测。
Welcome to the course "Logistic Regression for Predictive Modeling"! In this course, we will delve into the powerful statistical technique of logistic regression, a fundamental tool for modeling binary outcomes. From analyzing advertisement data to predicting credit risk, you'll gain hands-on experience applying logistic regression to real-world datasets. Get ready to unlock the predictive potential of your data and enhance your analytical skills!Section 1: Introduction This section provides an overview of logistic regression, a powerful statistical technique used for modeling the relationship between a binary outcome and one or more independent variables.Section 2: Advertisement Dataset Exploration of a dataset related to advertisements, covering topics such as data preprocessing, feature scaling, and fitting logistic regression models to predict outcomes.Section 3: Diabetes Dataset Analysis of a diabetes dataset, including logistic regression modeling, dimension reduction techniques, confusion matrix interpretation, ROC curve plotting, and threshold setting.Section 4: Credit Risk Examining credit risk through a dataset involving loan status, applicant income, loan amount, loan term, and credit history. Students learn how to split datasets for training and evaluation purposes.In this course, students will:Gain a solid understanding of logistic regression, a statistical method used for binary classification tasks.Learn how to preprocess and explore real-world datasets, such as advertisement and diabetes datasets, to prepare them for logistic regression analysis.Explore various techniques for feature scaling, dimension reduction, and model fitting to optimize logistic regression models for accurate predictions.Understand how to evaluate the performance of logistic regression models using key metrics like confusion matrices, ROC curves, and area under the curve (AUC).Apply logistic regression to practical scenarios, such as credit risk assessment, by analyzing relevant features like dependents, applicant income, loan amount, loan term, and credit history.Gain hands-on experience with data manipulation, model building, and evaluation using tools like Python, pandas, scikit-learn, and matplotlib.Overall, students will develop the skills and knowledge necessary to apply logistic regression effectively in various domains, making data-driven decisions and predictions based on binary outcomes.
