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Feature Description
The Titanic Survival Prediction uses machine learning to predict whether a passenger survived the infamous sinking of the Titanic based on various features such as age, gender, passenger class, fare, and more. The project involves data preprocessing, feature engineering, model selection, and training using classification algorithms. Initially, the dataset is cleaned to handle missing values and encode categorical variables. Features that are likely to impact survival chances are carefully selected and transformed. Various machine learning models such as Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines are trained and evaluated to find the best performing model.
Use Case
an educational tool for data scientists and machine learning enthusiasts to understand the full lifecycle of an ML project, from data cleaning and feature engineering to model evaluation and deployment. It provides practical experience in handling real-world datasets and building predictive models. Additionally, this project can be used in academic settings to teach students about historical data analysis and the application of machine learning techniques to solve classification problems.
Benefits
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Priority
High
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