• 👋 Hi, I’m @Prathamesh-dev-code
• 👀 I’m interested in Machine Learning Projects
• 🌱 I’m currently learning more about data science
• 📫 How to reach me (www.linkedin.com/in/prathamesh-pandhare-7b8583255)

I successfully built a predictive model to determine whether passengers survived the historic sinking of the Titanic. Leveraging a dataset that included passenger attributes like name, age, sex, socio-economic class, and more, I aimed to answer the question: "What types of people were more likely to survive?" To achieve this, I carefully selected a predictive model and used a voting ensemble method to enhance its accuracy. After thorough experimentation, it became evident that the XGBoost (XGB) model performed the best, delivering an impressive accuracy rate of 81%. Additionally, I explored automated machine learning (AutoML) techniques, which yielded results on par with the XGB model. The dataset featured essential variables such as passenger class (Pclass), gender (Sex), age (Age), family relations (Sibsp and Parch), ticket information (Ticket), fare (Fare), cabin number (Cabin), and port of embarkation (Embarked). Pclass served as a proxy for socio-economic status, and age values were sometimes in fractional form for estimated ages.