• Jayesh Dipak Kulkarni
• Prof. Sachin Shinde
• Dr. Sonali Patil

The prediction of survival outcomes from the tragic sinking of the Titanic remains a significant case study in data science and statistical analysis. This study aims to leverage modern machine learning techniques to predict the likelihood of survival for passengers aboard the Titanic based on various features such as age, gender, class, fare, and embarkation point. Using a dataset of historical passenger information, we apply classification algorithms like logistic regression, decision trees, and random forests to model survival predictions. Additionally, we explore feature engineering, model evaluation metrics, and comparison of algorithm performance to assess accuracy and robustness. The findings aim to shed light on the key factors influencing survival and demonstrate the potential of predictive analytics in historical event analysis.

Titanic Survival Prediction: A Machine Learning Approach

"Titanic Survival Prediction: A Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.6-7, August-2025, Available :http://www.jetir.org/papers/JETIRHA06002.pdf

"Titanic Survival Prediction: A Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. pp6-7, August-2025, Available at : http://www.jetir.org/papers/JETIRHA06002.pdf