Titanic

In this project, the dataset we use is the famous Titanic dataset on Kaggle (www.kaggle.com/c/titanic). We will run 10 common Machine Learning Algorithms to predict the survival rate of the passengers. The objective of this project is to increase the accuracy of the prediction by processing the dataset using feature engineering and to discover the the most well-proformed algorithm among the 10 machine learning methods. Our first step is to use exploratory data analysis (EDA) to look at the dataset closely to make sure we understand the dataset fully and then we can do the further cleaning and feature engineering to help the dataset fits our machine learning models well. In our last step, we found that the decision tree classifier returns the greatest value.