Titanic 相关的热门 GitHub AI项目仓库

马哈拉施特拉邦大学（SPPU）计算机工程专业三年级课程代码：机器学习（ML）与信息与网络安全（ICS）

这个程序拥有简洁美观的图形用户界面（GUI），它通过不同的Python库与8个机器学习模型和数据可视化工具进行交互。用户可以选择哪个模型来处理测试数据，然后程序会跳转到一个界面，显示测试数据的预测结果以及模型的整体准确率。该界面还包含多个按钮，点击后可以显示复杂而美观的测试数据可视化图表。

学习使用 Apache Spark 进行机器学习的详细笔记和代码

Exploring the World's Most Renowned Shipwreck ?

? Titanic User Survival Prediction using MLOps ? Automated ML pipeline with feature engineering & model training ? Dockerized microservices + Airflow DAGs for orchestration ? Prometheus-monitored web app for live survival predictions

深度学习的一些实用示例（.ipynb）

机器学习基础课程。使用泰坦尼克号数据集进行实践练习。

Titanic Survival Prediction Project (93% Accuracy)?️ In this notebook, The goal is to correctly predict if someone survived the Titanic shipwreck using different Machine Learning Model & Hyperparameter tunning.

这是一个使用Neural Network Console学习Kaggle泰坦尼克号数据集的示例。包含数据预处理（Jupyter Notebook）、模型结构自动搜索（Neural Network Console）和ONNX推理（Jupyter Notebook）三个部分。

The task is to predict whether a passenger was transported to an alternate dimension during the Spaceship Titanic's collision with the spacetime anomaly. To help us make these predictions, we are given a set of personal records recovered from the ship's damaged computer system.

本项目使用泰坦尼克号数据集进行机器学习和深度学习实践。机器学习部分涵盖数据预处理、数据可视化、模型构建和集成学习；深度学习部分涵盖PyTorch基础、PyTorchLightning框架和RayTune超参数调优。

该机器学习模型使用LightGBM算法，并基于“宇宙飞船泰坦尼克号”数据集进行训练。

✨ Titanic Survival Classification Using Titanic Dataset ✨ It involves data cleaning, feature engineering, and model building with Python’s Pandas, NumPy, and Scikit-learn libraries. Visualization tools like Matplotlib and Seaborn were used to uncover key insights through colorful, appealing charts. Overall, it demonstrates power of Data Science.

用于分析泰坦尼克号数据集的MCP数据服务器

An exploration of the Titanic dataset from Kaggle. The project includes training various Machine Learning models to predict Survival and the best performing one is used in a Flask app which generates predictions based on user input.

Binary classification pipeline for the Spaceship Titanic Kaggle challenge using XGBoost, AutoML, and feature engineering for high-accuracy predictions.

A machine learning project to predict whether passengers were transported to an alternate dimension in the Spaceship Titanic dataset. It involves data cleaning, model training with LGBM and XGBoost, and model performance comparison.