Survival Prediction & Exploratory Analysis (Titanic Dataset)

Analyzing passenger data to identify key factors influencing survival rates using statistical analysis and

data visualization.

Key Contributions:

Data Cleaning: Handled missing values (Age, Embarked) and performed feature engineering (Title

extraction, Family size).

Exploratory Data Analysis (EDA): Discovered strong correlations between survival, gender, and

socio-economic status (Class).

Visualization: Created clear charts using Seaborn and Matplotlib to communicate insights visually.

Modeling: Applied Data Preprocessing steps to prepare the dataset for Machine Learning models.

Tools Used:Python | Pandas | Seaborn | Scikit-Lear