🚢 Titanic Survival Analysis — Exploratory Data Analysis (EDA)

This project explores the Titanic dataset to uncover insights about passenger survival. Using Python libraries like Pandas, Seaborn, and Matplotlib, I performed data cleaning, transformation, and visualization to answer key questions such as:

• Who had higher chances of survival?
• How did age, sex, class, and fare influence survival?
• What patterns emerge through correlation and feature distribution?

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📊 Key Visual Insights

Plot	Description
Survival Count	Overall number of survivors vs non-survivors
Survival by Sex	Shows higher female survival rate
Survival by Class	Passengers in higher classes had better survival
Age Distribution	Provides an overview of passenger age spread
Age vs Survival (Boxplot)	Highlights survival tendencies based on age
Pairplot	Multivariate visualization of relationships between features
Correlation Heatmap	Correlation between key numerical features

📁 All output images are saved as .png in the project directory.

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⚙️ Technologies Used

Tool	Purpose
Python 3.9	Scripting and logic implementation
Pandas	Data wrangling and analysis
Matplotlib	Visualization and plots
Seaborn	Enhanced statistical graphics
NumPy	Efficient numerical computation

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🧹 Data Preprocessing

• Handled missing values in Age and Embarked
• Dropped Cabin due to excessive nulls
• Encoded Sex and Embarked for analysis
• Created cleaner feature set for visualization

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🚀 How to Run

# Clone the repo
git clone https://github.com/yuvraj0412s/Exploratory_Data_Analysis.git
cd Exploratory_Data_Analysis

# Make sure you have the dependencies installed
pip install pandas numpy matplotlib seaborn

# Run the script
python Task5.py