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GitHub topics: titanic-machine-learning

bayudwimulyadi/Titanic-Survival-Prediction

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.

Language: Jupyter Notebook - Size: 587 KB - Last synced at: about 22 hours ago - Pushed at: about 23 hours ago - Stars: 0 - Forks: 0

AAdewunmi/titanic_survival_project

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

Language: Python - Size: 102 KB - Last synced at: 11 days ago - Pushed at: 11 days ago - Stars: 0 - Forks: 0

RadhikaBailurkar/Titanic-Machine-Learning-from-Disaster

Kaggle Machine Learning Competition

Language: Jupyter Notebook - Size: 232 KB - Last synced at: 12 months ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 1

Aiza-D/EDA-of-Titanic.csv-

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

Size: 1.01 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

franklaercio/titanic_machine_learning

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

Language: Jupyter Notebook - Size: 223 KB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 2

ISAIAH-Git/Titanic-Machine-Learning-from-Disaster

END TO END MACHINE LEARNING MODEL BUILDING

Language: Jupyter Notebook - Size: 685 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0

marcpaulo15/titanic_streamlit

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

Language: Jupyter Notebook - Size: 286 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

SUDIPA9002/ASIP

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

Language: Jupyter Notebook - Size: 2.07 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

tamanna18/Titanic-Survival-prediction

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

Language: Jupyter Notebook - Size: 155 KB - Last synced at: almost 2 years ago - Pushed at: about 3 years ago - Stars: 9 - Forks: 0

keivanipchihagh/Titanic-Machine-Learning-from-disaster 📦

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

Language: Jupyter Notebook - Size: 602 KB - Last synced at: about 2 years ago - Pushed at: almost 4 years ago - Stars: 4 - Forks: 0

Nitin-Diwakar/Titanic-kaggle-Competition

Language: Jupyter Notebook - Size: 119 KB - Last synced at: about 2 months ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

SoundaryaMiranam/Survival_prediction_Titanic-disaster

Data analysis and Machine learning on titanic data

Language: Python - Size: 44.9 KB - Last synced at: about 2 years ago - Pushed at: about 4 years ago - Stars: 0 - Forks: 0

Eben2020-hp/Titanic-Machine-Learning

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

Language: Jupyter Notebook - Size: 526 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 1

Rishikeshrajrxl/Titanic-Machine-Learning-from-Disaster

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.

Language: Jupyter Notebook - Size: 148 KB - Last synced at: over 1 year ago - Pushed at: almost 5 years ago - Stars: 1 - Forks: 1

ozlemkorpe/Titanic-Machine-Learning-from-Disaster-MATLAB

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".

Language: MATLAB - Size: 511 KB - Last synced at: 3 months ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 0