重点:在kaggle notebook上时,应该把pd.read_csv("./kaggle/input/titanic/train.csv")引号中第一个'.'去掉 读入训练集和测试及都需要 train=pd.read_csv("./kaggle/input/titanic/train.csv")test=pd.read_csv("./kaggle/input/titanic/test.csv")
Titanic: Machine Learning from Disaster http://www.kaggle.com/c/titanic 目的是根据泰坦尼克号乘客的基本信息来推测其生还机率。在这里大家可以顺便把这次用的数据下载好。 获取数据 登陆Kaggle, 没有的话注册一个先。 在这个页面获取Titanic的数据 https://www.kaggle.com/c/titanic/data 同时下载test.csv, 和...
Python机器学习实践与Kaggle实战(转) https://mlnote.wordpress.com/2015/12/16/python%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%AE%9E%E8%B7%B5%E4%B8%8Ekaggle%E5%AE%9E%E6%88%98-machine-learning-for-kaggle-competition-in-python/ Author: Miao Fan (范淼),Ph.D. candidate on Computer ...
梯度提升机(Gradient Boosting Machine/GBM)效果最好,其次是简单的 logistic 回归。对于我们的集成策略来说,预测误差必须是相对不关联的。 # You need ML-Ensemble for this figure: you can install it with: pip install mlens from mlens.visualization import corrmat corrmat(P.corr(), inflate=False) plt....
导语:很多同学在学习机器学习时往往掉进了不停看书、刷视频的,但缺少实际项目训练的坑,有时想去练习却又找不到一个足够完整的教程,本项目翻译自kaggle入门项目Titanic金牌获得者的Kernel,该篇文章通过大家并不陌生的泰坦尼克数据集详细的介绍了如何分析问题、数据预处理、建立模型、特征选择、模型评估与改进,是一份不可...
7. #Import chi2 for performing chi square test from sklearn.feature_selection import chi2 8. #URL for loading the dataset 9. url ="https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians diabetes/pima-indians-...
Deep Learning Even though deep learning is a subsection Machine Learning, we created a separate section for this field as it has received tremendous attention recently with various acqui-hires by Google and Facebook. Theano Theano is the most mature of deep learning library. It pr...
Python is one of the best programming languages out there, with an extensive coverage in scientific computing: computer vision, artificial intelligence, mathematics, astronomy to name a few. Unsurprisingly, this holds true for machine learning as well. ...
在本文中,将使用 Kaggle Netflix prize 数据集来演示如何使用基于模型的协同过滤方法在 Python 中构建推荐系统。 本文其余部分安排如下: 协同过滤概述 用Python 构建推荐系统 总结 1、协同过滤概述 协同过滤背后的主要思想是,一个人经常从另一个兴趣相似的人那里得到最佳推荐。协同过滤使用各种技术来匹配兴趣相似的人,...
了解该竞赛可参阅:https://towardsdatascience.com/machine-learning-kaggle-competition-part-one-getting-started-32fb9ff47426,完整数据集可在这里下载:https://www.kaggle.com/c/home-credit-default-risk/data。这里我们将使用部分数据样本来进行演示。