Titanic-Survival-Prediction

This program predicts the survival of the passengers after the clash of the Titanic ship. The dataset used in this program has 12 columns and 891 rows in which many are missing. Moreover, the dataset has a combination of Categorical values which need to be converted to numeric and Numerical data with vast differences which need to be scaled for good predictions. In order to address all these issues, I am going to firstly remove unnecessary columns and fill in the missing values of strings with mode and numbers with mean of the respective columns. Later, I am going to scale the data using the MinMaxScaler. After the Data Preprocessing, I am going to apply Decision Tree Classifier as my machine learning model, report feature importance, and improve my results by considering dropping least important features as well as applying other machine learning models like Linear Regression, Random Forest, and KNN as well.