Introduction

I registered at Kaggle and did the Titanic Challenge. Kaggle is a programming challenge platform for machine learning and data analytics. This is a writeup of the Titanic challenge. In this challenge we are building a probabilistic model to predict the survival of passengers based on their gender, age, and socio-economic status. If you want to follow along with a jupyter notebook, you can download it from here.

Coding

Kaggle provides us with the file train.csv for training our model. There is also a file test.csv of passengers whose survival we have to predict and upload to the platform.

Loading libraries

First, we load all the necessary libraries.

import pandas as pd
from pandas_profiling import ProfileReport
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split

Loading data

Next, we load the data provided by Kaggle and explore it.

train = pd.read_csv('kaggle/train.csv')
test = pd.read_csv('kaggle/test.csv')

The various columns of the data are as follows:

• survival: 0 = No, 1 = Yes
• pclass: Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd
• sex: Sex
• Age: Age in years
• sibsp: # of siblings / spouses aboard the Titanic
• parch: # of parents / children aboard the Titanic
• ticket: Ticket number
• fare: Passenger fare
• cabin: Cabin number
• embarked: Port of Embarkation

train

train.describe()

profile = ProfileReport(train, title="Pandas Profiling Report", explorative = True)
profile.to_notebook_iframe()