Titanic Survival Analysis

This project explores the Titanic dataset and analyzes factors that influenced passenger survival. It demonstrates data exploration, visualization, and basic predictive insights using Python.

##📂 Project Structure

titanic-survival-analysis/ │── titanic_analysis.py # Main script with analysis │── README.md # Project overview

📝 Dataset

• Source: Kaggle Titanic Dataset
• Features include passenger demographics, ticket info, class, and survival status.

🔍 Analysis Highlights

• Exploratory Data Analysis (EDA) to understand trends and distributions.
• Visualizations of survival rates by age, gender, class, and other factors.
• Insights on which features had the most impact on survival.

🛠 Tools & Libraries

• Python 3.x
• pandas, numpy
• matplotlib, seaborn

📊 Sample Insights

• Gender: Females had a higher survival rate than males.
• Passenger Class: Higher class passengers were more likely to survive.
• Age: Children had slightly higher survival chances.

🚀 How to Run

1. Clone the repository:

   git clone https://github.com/afroz599/titanic-survival-analysis.git
   

2. Navigate to the folder:

cd titanic-survival-analysis

3. Run the analysis script:

python titanic_analysis.py

📌 Future Work

Add predictive modeling (Logistic Regression, Random Forest).

Improve visualizations and interactivity with Plotly or Dash.

Made with ❤ by Afroz Mohommad