Project 1: Analyzing Titanic Survivors

The Titanic is undoubtedly one of the most infamous maritime disasters in history, and the analysis of its passenger data has been a topic of interest for many data enthusiasts. This project report provides a comprehensive analysis of the attributes of the Titanic passengers and their survival rates after the disaster.

The report starts by describing the data source and its attributes, with a significant number of missing values in the age and cabin columns. The descriptive statistics indicate that the majority of passengers were young, with more passengers in the 3rd class. Most passengers did not survive, with a high proportion of male passengers among those who perished.

The data visualization techniques employed in the report highlight some interesting patterns, such as the higher survival rate of women and higher-class passengers. The correlation analysis further emphasizes the importance of age and sex in determining survival rates.

The machine learning models, particularly the Decision Tree and Random Forest models, achieve high accuracy rates in predicting the survival of passengers. The creation of a neural network model further strengthens the accuracy of the predictions, with Rose having a significantly higher chance of survival than Jack.

Overall, this project report provides valuable insights into the factors that contributed to the survival of Titanic passengers. The results show that gender, age, and socio-economic status played a significant role in determining the survival rate.

Find the full project code here: navyasrivattikuti/TitanicTensorflow (github.com)

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