لم يكتب نبذة شخصيةThis project focuses on analyzing the Titanic passenger dataset using Python and the Pandas library. The goal is to explore the data, clean it, and extract meaningful insights about the factors that influenced passenger survival. The dataset was first loaded and explored to understand its structure, data types, and missing values. Data cleaning techniques were applied, including handling missing values in the Age column. A new feature called FamilySize was created to represent the total number of family members traveling with each passenger. Several data analysis techniques were applied, such as filtering, grouping, aggregation, and pivot tables, to study patterns in the data. The analysis included calculating the survival rate by gender and passenger class, which helped highlight the impact of social and demographic factors on survival chances. Tools & Technologies: Python, Pandas, NumPy.