https://github.com/tushar365/titanic---machine-learning-from-disaster

"Titanic: Machine Learning from Disaster" is a classic Kaggle competition for beginners https://www.kaggle.com/competitions/titanic. The goal is to use machine learning to predict which passengers survived the sinking of the Titanic based on historical data. This teaches data analysis and model building skills in a real-world context.
https://github.com/tushar365/titanic---machine-learning-from-disaster

data-science dicision-tree logistic-regression machine-learning matplotlib-pyplot numpy pandas python seaborn

Last synced: about 1 month ago
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"Titanic: Machine Learning from Disaster" is a classic Kaggle competition for beginners https://www.kaggle.com/competitions/titanic. The goal is to use machine learning to predict which passengers survived the sinking of the Titanic based on historical data. This teaches data analysis and model building skills in a real-world context.

• Host: GitHub
• URL: https://github.com/tushar365/titanic---machine-learning-from-disaster
• Owner: Tushar365
• Created: 2023-02-03T13:05:31.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-05T15:37:47.000Z (8 months ago)
• Last Synced: 2024-05-05T16:42:17.130Z (8 months ago)
• Topics: data-science, dicision-tree, logistic-regression, machine-learning, matplotlib-pyplot, numpy, pandas, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 735 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Titanic - Machine Learning from Disaster

This repository explores the tragic sinking of the RMS Titanic through the lens of machine learning. By analyzing passenger data, we aim to predict survival rates and gain insights into factors that influenced survival outcomes.

This project is ideal for:

Beginners in data science and machine learning

Individuals interested in the historical significance of the Titanic disaster

Anyone looking to practice data wrangling, analysis, and model building

What you'll find here:

Python scripts for data exploration, cleaning, and feature engineering

Machine learning models to predict passenger survival

Visualizations to understand relationships between features and survival

Code demonstrating common data science practices

Getting Started:

Clone this repository.

Ensure you have Python and necessary libraries installed (refer to requirements.txt).

Run the Python scripts sequentially to explore data, build models, and generate visualizations.

Learning Objectives:

Data wrangling techniques (handling missing values, creating new features)

Exploratory data analysis (finding patterns, correlations)

Building and evaluating machine learning models (classification)

Understanding factors that influenced survival on the Titanic

Further Exploration:

Experiment with different machine learning algorithms

Fine-tune hyperparameters for improved model performance

Analyze the impact of specific features on model predictions

Remember: The sinking of the Titanic was a human tragedy. While machine learning offers valuable insights, it cannot fully account for the chaotic nature of the disaster.

This project is for educational purposes only. Feel free to explore, modify, and learn from the provided code!