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所在平台: Udemy |
课程主页: https://www.udemy.com/course/predictive-modeling-with-python-examturf/
课程评论：没有评论
**Coursera 课程总结：Python 预测建模** 本课程将深入介绍使用 Python 进行预测建模。您将了解预测建模如何利用数据和统计来预测结果，并将其应用于体育、电视收视率、企业收益和技术进步等各个领域。通过预测分析，您可以将数据与当前状况和未来事件的有效行动联系起来，并利用历史数据中的模式来识别潜在风险和机遇。 **课程亮点：** * **Python 的优势：** 掌握 Python 在预测建模中的应用，了解其快速出结果和指导下一步行动的优势。 * **预测思维培养：** 课程强调培养预测性思维能力和批判性思维，帮助您验证模型并解读结果。 * **核心技术学习：** 您将学习重要的预测建模技术，包括： * 线性回归 * 逻辑回归 * 使用 scikit-learn 和 statsmodels 库拟合模型 * ROC 曲线 * 向后消除法 * **实践操作：** 课程将指导您完成软件安装、数据预处理（包括数据框、数据集划分、特征缩放等）以及实际项目，涵盖： * 薪资预测 * 信用风险分析 * 糖尿病数据分析 完成本课程后，您将对 Python 预测建模有扎实的基础知识和实践经验，能够运用预测性思维分析和解决实际问题。
Predictive Modeling is the use of data and statistics to predict the outcome of the data models. This prediction finds its utility in almost all areas from sports, to TV ratings, corporate earnings, and technological advances. Predictive modeling is also called predictive analytics. With the help of predictive analytics, we can connect data to effective action about the current conditions and future events. Also, we can enable the business to exploit patterns and which are found in historical data to identify potential risks and opportunities before they occur. Python is used for predictive modeling because Python-based frameworks give us results faster and also help in the planning of the next steps based on the results.Our course ensures that you will be able to think with a predictive mindset and understand well the basics of the techniques used in prediction. Critical thinking is very important to validate models and interpret the results. Hence, our course material emphasizes on hardwiring this similar kind of thinking ability. You will have good knowledge about the predictive modeling in python, linear regression, logistic regression, the fitting model with a sci-kit learn library, the fitting model with stat model library, ROC curves, backward elimination approach, stats model package, etc.In this course, you will get an introduction to Predictive Modelling with Python. You will be guided through the installation of the required software. Data Pre-processing, which includes Data frame, splitting dataset, feature scaling, etc. You will gain an edge on Linear Regression, Salary Prediction, Logistic Regression. You will get to work on various datasets dealing with Credit Risk and Diabetes.
