https://github.com/sunnybibyan/exploratory-data-analysis-eda

Welcome to the Titanic Dataset - Exploratory Data Analysis (EDA) project repository! This project aims to uncover insights from the Titanic dataset using Python and Jupyter Notebook. By analyzing key variables such as age, gender, and class, we aim to visualize relationships between passenger characteristics and survival rates.
https://github.com/sunnybibyan/exploratory-data-analysis-eda

data-analysis data-visualization jupyter-notebook python titanic-dataset

Last synced: 7 days ago
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Welcome to the Titanic Dataset - Exploratory Data Analysis (EDA) project repository! This project aims to uncover insights from the Titanic dataset using Python and Jupyter Notebook. By analyzing key variables such as age, gender, and class, we aim to visualize relationships between passenger characteristics and survival rates.

• Host: GitHub
• URL: https://github.com/sunnybibyan/exploratory-data-analysis-eda
• Owner: SunnyBibyan
• License: mit
• Created: 2024-09-09T13:47:14.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-09T14:30:12.000Z (about 2 months ago)
• Last Synced: 2024-10-09T17:21:22.425Z (29 days ago)
• Topics: data-analysis, data-visualization, jupyter-notebook, python, titanic-dataset
• Language: Python
• Homepage:
• Size: 47.9 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Titanic Dataset - Exploratory Data Analysis (EDA)

## Project Overview:

This project performs a detailed **Exploratory Data Analysis (EDA)** on the Titanic dataset using Python libraries. The goal is to uncover insights about the factors that influenced the survival of passengers during the Titanic disaster.

## Objectives:

- Clean and preprocess the Titanic dataset.

- Perform univariate, bivariate, and multivariate analyses.

- Visualize data insights using plots.

## Dataset:

The Titanic dataset can be found on [Kaggle](https://www.kaggle.com/datasets/yasserh/titanic-dataset).

## Tools and Technologies:

- **Pandas**: Data manipulation and cleaning.

- **Numpy**: Numerical operations.

- **Seaborn**: Data visualization.

- **Matplotlib**: Plotting and visualization.

## Key Insights:

- **Gender**: Females had a much higher survival rate compared to males.

- **Class**: First-class passengers were more likely to survive.

- **Age**: Younger passengers had a higher survival rate.

## How to Run the Project:

1. Clone the repository:

   ```bash

   git clone https://github.com/SunnyBibyan/Exploratory-Data-Analysis-EDA.git

2. Install the required libraries:

   ```bash

   !pip install -r requirements.txt