In this challenge, I build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).

Kaggle Titanic with logexp

Assignment of the "Introduction to Data Science" course on Coursera.org

Анализ данных о пассажирах Титаника и разбиение на обучающую и тестовую выборки. Практическое задание по дисциплине "Основы применения методов искусственного интеллекта в программировании".

All things related to deep learning.

Проект по обработке пропущенных значений в данных о пассажирах Титаника с использованием библиотек Python Matplotlib и Seaborn.

Python script that can predict a survival on the Titanic.

Predicts Titanic passenger survival using machine learning (Logistic Regression, Decision Tree, and Random Forest). Analyzes factors like age, gender, and fare to identify key predictors.

Titanic Dataset

🚢 End-to-end Python pipeline for Titanic survival classification. Demonstrates EDA, preprocessing, feature engineering, and Logistic Regression evaluation using Scikit-learn.

Build a model with machine learning to predict which passengers would survive the Titanic's maiden voyage.

Classifier to predict if you'd die on the Titanic

Estudo de machine learning no dataset do titanic

Django implementation of an SVC classifier

Проект по визуализации данных о пассажирах Титаника с использованием библиотек Python Matplotlib, Seaborn и Plotly.

The classic Kaggle Titanic data science challenge

Solve simple data science problem, finding most freq names.

A simple webapp that displays information about the deathtoll of the Titanic using the Titanic Dataset. The webapp is created using the Python3 library 'streamlit'. The app contains a few filters such as the Pclass, Gender and Survived(0 or 1) that display the data according to those filters selected.

Kaggle Titanic Survivors Dataset solution