Start here if... You're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions. Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. Practice Skills Binary classification Python and R basics

applying data analysis on titanic data sheet

Exploratory Data Analysis on Titanic Survivor Dataset provided by Kaggle.

Kaggle Competition Question

Data Visualization for titanic data

Data analysis and Machine learning on titanic data

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.

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

No description provided.

Проект по анализу и визуализации данных о пассажирах Титаника с использованием библиотек Pandas, Scikit-learn, Matplotlib, Seaborn и Plotly. Включает в себя Feature Engineering, обработку пропущенных значений и создание интерактивных визуализаций.