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所在平台: Udemy |
课程主页: https://www.udemy.com/course/credit-risk-prediction-project-from-scratch-in-python/
课程评论：没有评论
课程名称：从零开始的信用风险预测项目（Python） 课程概述： 本课程分为两个部分：问题陈述解释与解决方案解释，包括源代码。 第一部分：介绍信用风险预测项目，详述即将构建的项目的细节和步骤。该项目旨在基于银行提供的数据，通过历史分析预测客户违约的可能性。完成此项目后，我们将能够预测具有特定资质的个人成为违约者或成功借款人的几率。 第二部分：在Kaggle社区平台上创建完整的信用风险预测项目，基于客户的资质预测信用失败。我们将进行数据清洗、数据可视化，并利用随机森林分类器、支持向量机和逻辑回归等算法，采用最佳参数以实现最佳预测准确性。所有这些算法都是数学实现，且我们在应用时进行了最优参数的调整。 适合人群： 本课程适合有志于学习机器学习的学生，尤其是那些在寻找有趣项目想法和构建项目方法上遇到困难的学生。课程将指导学生如何构建机器学习项目，以及如何找到能够激励自己的数据科学或机器学习项目创意。学生可以根据自身兴趣选择项目领域和数据集，并考虑数据集的大小和复杂度。如果你是新手或初学者，建议从专注于数据清洗的机器学习项目开始，然后再逐步深入到分析、机器学习和深度学习中。 感谢与祝福 Jitendra
This course consist of two parts: Problem statement explanation and Solution explanation with source code. Part 1: This is the introduction part of the CREDIT RISK PREDICTION Project where we provide the details and procedures of the coming project that we will build in Part2 of this Project. This is based on prediction of defaulters in bank credit based on the data provided by the bank using past analysis. The result of this project will be that we will be able to forecast what are the chances of a person with certain credentials that will be a defaulter or a successful player.Part 2: This is the second part of the CREDIT RISK PREDICTION Project where we create a complete project on Kaggle Community Platform regarding prediction of Credit Failure of customers based on their credentials. We use data cleaning, data plotting and utilised Random Forest Classifier, Support Vector Machine and Logistic Regression with best parameters possible for getting the best prediction accuracy. All these algorithms are mathematical implementations and we have utilised them with optimal parameters.Whom is This Course for?Aspiring machine learning students want to learn on machine learning projects but struggle hard to find interesting ideas and how to build the project. How should students build Machine learning projects, find data science or machine learning project ideas that motivate you, when deciding on a machine project to get started. You can decide the domain and dataset based on your interest. Size of the dataset and complexity of the dataset. If you are a fresher or a beginner, We recommend you get started with ML projects that focus on data cleaning and then move on to analytics, machine learning, and deep learningThanks & RegardJitendra
