Titanic Data Analysis and Visualization Project

A comprehensive Jupyter notebook-based data analysis and visualization project exploring the Titanic dataset using Python data science libraries, the dataset was downloaded from Kaggle.

Overview

This project performs in-depth exploratory data analysis (EDA) and visualization on the Titanic dataset. It includes data loading, cleaning, missing value analysis, and various statistical and visual explorations to understand patterns and relationships in the data.

Dataset

The project uses the classic Titanic dataset with two CSV files:

• train.csv: Training dataset with survival outcomes
• test.csv: Test dataset for predictions

Located in the Data/ directory.

Project Structure

Titanic_Visualization_Project/
├── titanic_analysis.ipynb          # Main Jupyter notebook with all analysis
├── Data/
│   ├── train.csv                   # Training dataset
│   └── test.csv                    # Test dataset
├── virtual_enviroment/             # Python virtual environment
└── README.md                        # This file

Requirements

The project uses a Python virtual environment with the following key libraries:

• pandas - Data manipulation and analysis
• numpy - Numerical computing
• matplotlib - 2D plotting and visualization
• seaborn - Statistical data visualization
• jupyter - Interactive notebook environment

Installation & Setup

1. Create/Activate Virtual Environment

If you haven't already set up the virtual environment:

python -m venv virtual_enviroment

Activate the virtual environment:

Windows (PowerShell):

.\virtual_enviroment\Scripts\Activate.ps1

Windows (Command Prompt):

.\virtual_enviroment\Scripts\activate.bat

Mac/Linux:

source virtual_enviroment/bin/activate

2. Install Dependencies

pip install pandas numpy matplotlib seaborn jupyter notebook

Usage

Running the Notebook

Start Jupyter Notebook:

jupyter notebook

Then open titanic_analysis.ipynb in your browser.

Alternatively, you can use Jupyter Lab:

jupyter lab

Notebook Contents

The analysis notebook includes the following sections:

1. Library Imports - Loads all required data science libraries
2. Version Check - Displays library versions
3. Data Loading - Imports and displays training and test datasets
4. Basic Information - Dataset shape, columns, and data types
5. Missing Values Analysis - Identifies and quantifies missing data
6. Missing Values Visualization - Heatmaps and bar charts showing data gaps
7. Statistical Analysis - Summary statistics and distributions
8. Survival Analysis - Examination of survival rates across different features
9. Passenger Demographics - Analysis of age, gender, class distributions
10. Correlations & Relationships - Statistical relationships between variables
11. Advanced Visualizations - Multiple visualization types for deeper insights

Key Analysis Areas

• Data Quality: Missing value patterns and data completeness
• Passenger Demographics: Age, gender, class analysis
• Survival Patterns: Factors influencing survival outcomes
• Correlations: Relationships between different variables
• Visualizations: Histograms, heatmaps, scatter plots, and more

Technologies Used

• Python 3.x - Programming language
• Jupyter Notebook - Interactive computing environment
• Pandas - Data manipulation
• NumPy - Numerical operations
• Matplotlib - Plotting library
• Seaborn - Statistical visualization

Notes

• The notebook includes informative print statements for tracking data loading and processing steps
• Warnings are filtered to maintain clean output
• All visualizations are embedded directly in the notebook

Author

Created for data analysis and visualization practice.

License

This project uses the publicly available Titanic dataset.

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For questions or improvements, feel free to modify and expand this project!