🚢 Titanic Data Analytics Project

Author: Dhimant Adhikari

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📋 Table of Contents

• Objective
• Project Components
• Key Findings
• Recommendations
• Dependencies
• Usage
• Visualizations
• License
• Contact
• Acknowledgments

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🎯 Objective

The primary goal of this project is to evaluate whether the survival rate on the Titanic was influenced by passenger class and to analyze age distribution within each class to identify vulnerable age groups. The analysis aims to determine whether age played a significant role in passengers' survival chances.

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🛠️ Project Components

• SQL: Utilized for efficient data extraction and management, ensuring precise retrieval of relevant data subsets for further analysis.
• Python:
  • Data Processing: Used Pandas for data manipulation, cleaning, and preparation.
  • Statistical Analysis: Conducted survival rate analysis across different passenger classes and age groups.
  • Visualization: Created visualizations with Matplotlib and Seaborn to uncover trends and correlations.
• Tableau: Developed an interactive dashboard that visually represents survival rates and age distributions across different passenger classes, allowing for easy exploration of the data.

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📊 Key Findings

• Class Disparities:

  • Significant disparities exist in survival rates among different passenger classes.
  • First-Class passengers had nearly three times the survival rate of Third-Class passengers.
  • Highlights the importance of analyzing class-specific survival percentages.

• Age Distribution Impact:

  • Children:
    • Second-Class children had a 100% survival rate.
    • First-Class children had an 80% survival rate.
    • Third-Class children had a 40% survival rate.
  • Seniors:
    • No seniors survived in the Second and Third classes.
    • Only 17% of seniors survived in First Class.
  • Age significantly impacted survival chances across different classes.

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💡 Recommendations

1. Minimize Class Disparity:

   • Efforts should be made to reduce the survival rate gap between different passenger classes.
   • Implement equitable safety measures to ensure all passengers have similar survival opportunities.

2. Improve Emergency Accessibility:

   • Investigate and enhance emergency protocols and accessibility measures.
   • Focus on improving survival rates across all age groups and passenger classes.

3. Guidance for Ship Design:

   • Utilize insights from this analysis to guide further research.
   • Design ships with equitable safety provisions for all passengers, regardless of class or age.

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📦 Dependencies

• Programming Languages:

  • Python 3.x
  • SQL (SQLite, MySQL, or any preferred SQL database)

• Python Libraries:

  • Pandas
  • Matplotlib
  • Seaborn

• Tools:

  • Tableau (for dashboard creation and visualization)

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🚀 Usage

1. Clone the Repository:

   git clone https://github.com/dhimant2299/titanic-data-analytics.git