Data link:https://www.kaggle.com/c/titanic/data a. Understanding data #导入数据包fromsklearnimportsvmimportpandasaspdimportnumpyasnu#将下载后的数据集进行数据训练importosos.chdir(r'E:\Titanic\titanic')#训练数据集train=pd.read_
Catalog:1.导数据/导包 2.查看缺失数据信息,填充缺失值 3.对7个数据分类:one-hot编码--按表头顺序--重点占比50% 4.特征选择 5.构建模型--训练数据集和测试数据集 6.选择算法 7.预测生成结果,提交kaggle机器学…
Kaggle · Getting Started Prediction Competition · Ongoing Join Competition more_horiz Titanic - Machine Learning from Disaster Start here! Predict survival on the Titanic and get familiar with ML basicsOverviewDataCodeModelsDiscussionLeaderboardRules...
Titanic Survival Prediction Project This project uses the Titanic dataset to predict whether a passenger survived or not based on various features such as age, gender, fare, class, and titles derived from names. The goal is to apply feature engineering and train multiple classification models to ...
Titanic Survival Prediction App A web application that predicts how likely you would have survived the Titanic disaster based on various features. This app uses a machine learning model to make predictions and visualizes the results using t-SNE plots. Features Predict survival likelihood based on us...
Explore and run machine learning code with Kaggle Notebooks | Using data from Titanic - Machine Learning from Disaster
35.4s 30 /opt/conda/lib/python3.7/site-packages/traitlets/traitlets.py:2935: FutureWarning: --Exporter.preprocessors=["remove_papermill_header.RemovePapermillHeader"] for containers is deprecated in traitlets 5.0. You can pass `--Exporter.preprocessors item` ... multiple times to add items to a...
The Titanic challenge hosted by Kaggle is a competition in which the goal is to predict the survival or the death of a given passenger based on a set of variables describing him such as his age, his sex, or his passenger class on the boat. I have been playing with the Titanic dataset...
sns.set_style("whitegrid") sns.plt.plot(roc_survival[0], roc_survival[1]) sns.plt.show() Logistic Regression using Scikit-Learn Here we shall learn how to perform modeling using sci-kit-learn: from sklearn import metrics from sklearn.linear_model import LogisticRegression log_sci_model ...
For my analysis, I started by doing some simple proportion tables to see what impact different categorical features had on survival. You can see my code on Github for all the details. Passenger Class and Sex were the most obvious features to test since they have 3 and 2 factors respectively...