Popular GitHub Repositories for Titanic

SPPU BE COMP LP3 Codes - Machine Learning (ML) and Information and Cyber Security (ICS)

This program consists of clean and polished Graphical User Interface (GUI) that interacts with 8 Machine Learning models and data visualization tools through the use of different Python libraries. The user can interact with the GUI through selecting which model to run on the testing data on, which then takes them to a screen displaying the prediction results of the testing data as well as the general model accuracy. The screen also includes various buttons that, when selected, display complex and attractive data visualizations on the testing data.

Detailed notes and code to learn machine learning with Apache Spark.

Some useful examples of Deep Learning (.ipynb)

Imagine boarding the Titanic in 2021, and you have provided all your details as a passenger to the captain. There is are three people involved, the data scientist, captain and the passenger. Imagine the company who has built Titanic has created a machine ML model to predict the rate of survival of the passengers, in case of a disaster. The job of the data scientist is to make a model that is explainable to the passengers who are not technical, and that they get the answer about the reasons why they may not survive.

Course on the fundamentals of machine learning. Practical experience tasks with the Titanic dataset.

Neural Network ConsoleでKaggleのタイタニックを学習するサンプルです。前処理(Jupyter Notebook)、学習?モデル構造自動探索(Neural Network Console)、ONNX推論(Jupyter Notebook)を含みます

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.

Implementation of an ML Model Serving with Flask, the model is LGBM trained on Kaggle titanic data.

Simple application with API endpoints to interact with machine learning models trained on the Titanic dataset.

We use the Titanic dataset to implement machine learning and deep learning. Preprocessing data, visualizing, building models, and ensembling are practiced in the ML section; PyTorch basics, PyTorchLightning framework, and RayTune hyperparameter-tuning are in the DL section.

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。

Production Level MLOps Project for Titanic Dataset

用于分析泰坦尼克号数据集的MCP数据服务器

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.