https://github.com/mgobeaalcoba/survival_predictor_on_the_titanic_scikit_learn

Titanic Survival Predictor using Scikit-Learn: Machine learning model and analysis to predict passenger survival on the Titanic based on historical data.
https://github.com/mgobeaalcoba/survival_predictor_on_the_titanic_scikit_learn

matplotlib numpy pandas python3 scikit-learn seaborn titanic-dataset titanic-kaggle titanic-survival-prediction

Last synced: about 2 hours ago
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Titanic Survival Predictor using Scikit-Learn: Machine learning model and analysis to predict passenger survival on the Titanic based on historical data.

• Host: GitHub
• URL: https://github.com/mgobeaalcoba/survival_predictor_on_the_titanic_scikit_learn
• Owner: Mgobeaalcoba
• Created: 2023-09-27T13:25:30.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-27T13:31:14.000Z (about 1 year ago)
• Last Synced: 2024-05-08T00:21:14.294Z (7 months ago)
• Topics: matplotlib, numpy, pandas, python3, scikit-learn, seaborn, titanic-dataset, titanic-kaggle, titanic-survival-prediction
• Language: Jupyter Notebook
• Homepage: https://colab.research.google.com/drive/17oOlyaizuNb_DiDqynnv-Ui2W64L_1y_#scrollTo=YNGsbPcP3nQU
• Size: 150 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Titanic Survival Predictor using Scikit-Learn

![Titanic](https://upload.wikimedia.org/wikipedia/commons/thumb/6/6e/St%C3%B6wer_Titanic.jpg/800px-St%C3%B6wer_Titanic.jpg)

This project was created in Google Colab and uses machine learning techniques to predict passenger survival on the Titanic based on historical data. The model achieved an accuracy of 81.5%.

## Overview

The Titanic Survival Predictor project aims to demonstrate the power of machine learning in predicting survival outcomes on the famous RMS Titanic. It utilizes popular Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-Learn.

## Dataset

The dataset used in this project is the [Titanic dataset](https://www.kaggle.com/c/titanic) from Kaggle. It contains passenger information such as age, gender, class, and whether or not they survived.

## Methodology

1. **Data Preprocessing**: Data cleaning, handling missing values, and feature engineering.

2. **Exploratory Data Analysis (EDA)**: Visualization using Matplotlib and Seaborn to gain insights into the data.

3. **Machine Learning**: Building a predictive model using Scikit-Learn classifiers. The model achieved an accuracy of 81.5%.

4. **Model Evaluation**: Assessing model performance through accuracy and other relevant metrics.