Title: Predicting Spaceship Titanic Passenger's Transport with Machine Learning
Objective: The objective of this project is to build a machine learning model that can accurately predict passenger Transport based on various features such as age, gender, class, fare, etc.
Kaggle Notbook Link: https://www.kaggle.com/code/kdsharma/spaceship-titanic-competition-end-to-end-project?scriptVersionId=119775965
Methodology: We will start by exploring the dataset and performing data cleaning, feature engineering, and visualization. Then, we will select and train several machine learning models, such as logistic regression, decision tree, random forest, and gradient boosting, and evaluate their performance using cross-validation and metrics such as accuracy, precision, recall, F1 score, and ROC AUC. We will also tune the hyperparameters of the best models using grid search or random search. Finally, we will select the best model and make predictions on a test set.
Expected Results: We expect to achieve an accuracy of at least 80% on the test set, indicating that our machine learning model can reliably predict Titanic passenger survival. We will also analyze the feature importance and generate insights into the factors that influenced passenger survival.
Conclusion: This project demonstrates the power of machine learning in analyzing historical data and predicting outcomes. It also highlights the tragedy of the Titanic and honors the memories of the passengers and crew who lost their lives.