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@article{194910,
        author = {M Ramakrishna Raju and M. Satya Sathvik Sai Varma and K. Srinivas and V. Venkatesh and P. Abhinay Varma},
        title = {Titanic Survival Prediction Using Machine Learning Classification for Survival Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8108-8112},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194910},
        abstract = {The use of machine learning in predictive analytics has greatly improved decision-making in different areas. One well- researched classification problem is predicting passenger survival in the Titanic disaster. This study aims to create a machine learning model that predicts whether a passenger survived based on demographic and travel-related information. The dataset for this research comes from Kaggle's Titanic dataset. We applied data preprocessing techniques, including handling missing values, encoding categorical variables, and feature selection, to enhance the model's performance. We chose Logistic Regression as the main classification algorithm because it is simple, easy to interpret, and effective for binary classification problems. We evaluated the model's performance using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The experimental results demonstrate that our system achieves reliable prediction accuracy and successfully identifies key survival factors including gender, passenger class, and age. Our system provides a scalable and efficient method for analyzing survival using machine learning techniques.},
        keywords = {Titanic Dataset, Survival Prediction, Machine Learning, Logistic Regression, Classification, Predictive Analytics.},
        month = {March},
        }