毕业设计论文范文源码-Machine-Learning-for-Startup-Investments:机器学习创业投资

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毕业设计论文范文源码用于初创投资的机器学习方法 完整数据集可从: 抽象的 在这个项目中,实施和探索了一种将初创公司分为两类(成功和不成功)的机器学习方法。 该项目的数据集是从 Crunchbase 获得的。 它由 11 个不同的表格组成,其中包含有关初创公司、投资者、关系和创始人在生态系统中的背景以及许多其他信息的信息。 四个表被列入候选名单并合并为一个数据集。 然而,在数据转换和预处理之后,由于数据稀疏,不得不丢弃大量数据。 最终数据集由 61,716 个初创公司实例和 36 个特征组成。 还进行了特征缩放,将特征数量减少到七个,同时保持相同的预测能力。 对数据使用了五种监督机器学习算法,其中包括:决策树、支持向量机、随机森林、朴素贝叶斯和多层感知器。 K-means Clustering 也被组合应用,用于提升性能。 所有机器学习算法的准确率都达到了 90% 以上。 然而,这可以归因于数据集上存在的类的偏态分布。 召回被认为是一个更重要的绩效指标,因为应该优先考虑最大限度地减少误报(将任何成功的启动错误归类为不成功)的策略(错失机会的成本非常高)。 结果表明,MLP 模型在所有其他

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