Titanic Survival prediction: Titanic dataset- how many people survive and how many were Male and Female

Predict who is likely to have survived in the titanic crash given his/her information

Start here! Predict survival on the Titanic and get familiar with ML basics.This is the legendary Titanic ML competition – the best, first challenge for you to dive into ML competitions and familiarize yourself with how the Kaggle platform works.

This project aims to predict the survival of passengers aboard the Titanic using the Naive Bayes classifier algorithm. The dataset used in this project contains information about Titanic passengers, such as their age, gender, passenger class, and other relevant features.

How to perform an exploratory data analysis on the Kaggle Titanic dataset.

The goal of this project is predicting the survival of passengers based on a set of data. Necessary data is retrieved from Kaggle competition "Titanic: Machine Learning from Disaster".

This project uses the famous Titanic dataset to predict passenger survival in the 1912 disaster. By applying machine learning classification techniques, the model learns from features to determine whether a passenger survived or not.

TOP13% solution for the Titanic-Kaggle competition using a Gradient Boosting Classifier. Moreover, implementation of a Streamlit App to play with the models.

Data analysis and Machine learning on titanic data

A data-driven analysis and machine learning model to predict passenger survival in the Titanic disaster using Python, Pandas, and Scikit-learn.

Data Science internship at Asterisc Technocrat Pvt. Ltd. Task - Billioniaire Analysis, Covid - 19 Data Analysis, Titanic Survival Prediction

Kaggle Machine Learning Competition

Detailed Exploratory Data Analysis (EDA) of the Titanic dataset.

Titanic Survival Prediction Project (Data Science) || Tech Stack: Python, Pandas, Numpy, Matplotlib, Seaborn, Scikit-Learn

In this code we will predict survived for the tragic accident Titanic. It's a Kaggle competition.

Predicting passenger survival on the Titanic using an ensemble machine learning approach, achieving a Kaggle score of 0.77990. This project leverages stacking with Random Forest, Gradient Boosting, and SVM, enhanced by feature engineering and hyperparameter tuning, to model survival patterns effectively.