🛳️ Titanic Survival Data Analysis: Factors Influencing Survival
🚀 Project Overview This project analyzes a simulated Titanic dataset to uncover trends and insights related to passenger survival rates. By employing statistical methods and interactive visualizations, we investigate key factors affecting survival, including:
Age: Did younger passengers have higher survival rates? Family Size: Did passengers with larger families have better chances of survival? Titles (Mr., Mrs., Miss., etc.): How did social status or titles influence survival? Embarkation Point: Did boarding location (Cherbourg, Queenstown, Southampton) affect survival chances? Fare: Was there a relationship between fare amount and survival? Gender: Did women and children have higher survival rates? Correlations: What relationships existed between different variables? Missing Data: Where are the gaps in the dataset, and how do they impact analysis?
🔍 What’s Inside? The repository includes a Jupyter Notebook containing step-by-step Titanic data analysis and the following visualizations:
Survival by Age Groups
A bar graph displaying survival rates across age categories (children, young adults, adults, and elderly). Survival by Family Size
Examines how family size (siblings, spouses, parents, or children aboard) influenced survival chances. Survival by Title
Analyzes survival rates based on social titles (e.g., Mr., Mrs., Miss.), reflecting the influence of social status. Survival by Embarkation Point
Explores survival differences based on embarkation points (Cherbourg, Queenstown, Southampton). Survival by Fare Distribution
Shows how fare amounts correlate with survival, reflecting class-based evacuation priorities. Survival by Gender
Compares survival rates between men and women, highlighting the prioritization of women and children in lifeboats. Correlation Heatmap
Visualizes the relationships between variables like age, family size, fare, and survival. Missing Values Heatmap
Highlights the locations of missing data to guide preprocessing efforts.
🌟 Key Insights from the Visualizations Missing Data: Understanding missing values is crucial for effective preprocessing. Age: Children showed higher survival rates due to prioritization. Family Size: Survival chances may increase with larger family groups. Title: Titles like “Mrs.” and “Miss.” correlated with higher survival, reflecting societal norms. Embarkation Point: Cherbourg passengers had slightly higher survival rates. Fare: Higher fares were associated with better survival odds, indicating class advantages. Gender: Women had significantly higher survival rates than men, aligning with historical accounts. Correlations: Relationships between variables such as fare, age, and survival provide actionable insights.
🧩 Technologies Used Jupyter Notebook 📓 for interactive analysis. Python 🐍 for data processing and analysis. Pandas 🐼 for data manipulation. Matplotlib and Seaborn 🎨 for visualizations. Numpy 🔢 for numerical operations.
📁 Dataset The dataset (Dataset.csv) is simulated to represent Titanic passenger data. It includes variables like age, gender, fare, family size, embarkation point, and survival status.
link : https://1drv.ms/x/c/f6bc90f75ca4b431/ERObFs-fyLNDqImvsFcaJkoBzihPx_iflqyeljRB7WdIJA?e=AnES5T
🎥 Watch the Project in Action Explore the analysis through the interactive notebook: https://1drv.ms/v/c/f6bc90f75ca4b431/EZP_VUyBdx9JndEwPtN-n9IB-SilnUGpVqiQp83o50ru6A?e=ptWQfG
🚀 Conclusion This project provides insights into simulated Titanic survival factors, demonstrating the use of data storytelling to analyze historical scenarios. By visualizing key variables, the analysis highlights patterns and relationships that could have influenced survival during the Titanic disaster.
💬 Contribute & Feedback Feel free to ask questions, suggest improvements, or contribute to the project. Contributions are always welcome!
