Explored the dataset by creating visualizations using Seaborn, Matplotlib and Pandas built in visualization libraries and cleaned the data by filling null values with relevant data. Performed analysis using cross tabulation and data segregation for the survivors against various factors such as ticket class, sex of the passenger, marital status, size of the family of the person onboard, name, ticket fare etc. to study the pattern of the survival rate. Concluded that the best fitted model for survival as response variable is based on ticket class, marital status, family size as independent variables.
ajita999 / titanic-machine-learning-from-disaster Goto Github PK
View Code? Open in Web Editor NEWExplored the dataset by creating visualizations using Seaborn, Matplotlib and Pandas built in visualization libraries and cleaned the data by filling null values with relevant data. Performed analysis using cross tabulation and data segregation for the survivors against various factors such as ticket class, sex of the passenger, marital status, size of the family of the person onboard, name, ticket fare etc. to study the pattern of the survival rate. Concluded that the best fitted model for survival as response variable is based on ticket class, marital status, family size as independent variables.