https://github.com/lu-sketch/eda---titanic-data-set

Did an EDA analysis for fun on the Titanic data set.
https://github.com/lu-sketch/eda---titanic-data-set

Last synced: 5 days ago
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Did an EDA analysis for fun on the Titanic data set.

• Host: GitHub
• URL: https://github.com/lu-sketch/eda---titanic-data-set
• Owner: lu-sketch
• Created: 2023-06-24T13:09:33.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-10T17:48:39.000Z (9 months ago)
• Last Synced: 2024-11-29T18:15:00.771Z (2 months ago)
• Language: Jupyter Notebook
• Size: 354 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Exploratory Data Analysis (EDA) on Titanic Dataset

This project involves conducting an exploratory data analysis (EDA) on the Titanic dataset. The analysis is performed for educational and recreational purposes to gain insights into the dataset and explore various trends and patterns.

## Overview

The Titanic dataset is a classic dataset used for data analysis and machine learning tasks. It contains information about passengers aboard the Titanic, including demographic information, ticket class, cabin, and survival status.

## Objective

The main objective of this project is to perform EDA to understand the characteristics of the Titanic dataset, uncover correlations between variables, and identify factors influencing survival rates.

## Tools and Libraries Used

- Python

- Jupyter Notebook

- Pandas

- NumPy

- Matplotlib

- Seaborn

## Analysis Steps

1. Data Loading and Inspection: Loading the dataset and inspecting its structure, including columns, data types, and missing values.

2. Data Cleaning: Handling missing values, removing irrelevant columns, and performing data transformations if necessary.

3. Exploratory Data Analysis: Conducting exploratory data analysis to visualize distributions, correlations, and patterns in the data.

4. Feature Engineering: Creating new features or transforming existing ones to enhance model performance.

5. Conclusion: Summarizing key findings and insights obtained from the analysis.

## Results

The analysis provides valuable insights into passenger demographics, survival rates, and factors affecting survival on the Titanic. Visualizations such as histograms, box plots, and correlation matrices are used to illustrate the findings.

## Conclusion

Through this EDA analysis, we gain a deeper understanding of the Titanic dataset and the factors influencing survival. This project serves as a foundation for further analysis and modeling tasks using machine learning algorithms.

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