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Using supervised machine learning to predict survivavibility of Titanic passangers (from Kaggle Challenge)

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Titanic--Machine-Learning-from-Disaster

Using supervised machine learning to predict survivavibility of Titanic passangers (from Kaggle Challenge)

The link to the challenge is linked below: https://www.kaggle.com/c/titanic

Approach

A. Data Processing
Based on given a dataset of titanic passengers, features were extracted such as age, gender, fare, cabin, and survival status.

Imputation and standardization/normalization of the raw dataset were applied to extract features.

B. Exploratory Analysis
Statstical Analysis was applied to find correlations between the survival status of passengers and the features.

  • Spearman and Pearson correlation
  • Point Bisceral Correlation
  • Chi-squared test

C. Machine Learning
Two (2) models were trained (XGBoost and Random Forest) with the processed dataset via hyperparameter tuning with 10-fold cross validation.

The best model (Random Forest) was chosen based on the Accuracy score of the model when the test data was used.

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Using supervised machine learning to predict survivavibility of Titanic passangers (from Kaggle Challenge)

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