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Titanic Data Analysis

This repository contains a Jupyter Notebook titanic_data_analysis.ipynb that showcases a Python data analysis project focused on exploring a synthetic dataset reminiscent of the Titanic dataset. The project employs data cleaning, exploratory data analysis (EDA), and visualization techniques to uncover insights into various factors influencing survival rates aboard the Titanic.

Overview

The main goal of this project is to analyze the synthetic Titanic-like dataset and gain insights into the following aspects:

  • Survival rates based on passenger class, gender, and age
  • Relationships between variables such as fare and passenger class
  • Distribution of age among passengers and its impact on survival

Key Features

  • Data Cleaning: The dataset undergoes thorough cleaning to handle missing values and ensure data quality.
  • Exploratory Data Analysis (EDA): Descriptive statistics and visualizations such as histograms, bar charts, and scatter plots are used to explore relationships between variables and identify patterns in the data.
  • Visualization: Various visualization techniques are employed to present findings in a clear and concise manner.

How to Use

  1. Clone this repository to your local machine.
  2. Open the titanic_data_analysis.ipynb file in Jupyter Notebook or JupyterLab.
  3. Run the notebook cells to perform data analysis on the provided dataset.
  4. Explore the generated visualizations and insights to gain a deeper understanding of the dataset.

Requirements

  • Python 3.x
  • Jupyter Notebook or JupyterLab
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn

Contribution

Contributions are welcome! If you find any issues or have suggestions for improvement, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.