Competition description : The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.

One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.

In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.

1. Load libraries and read the data

1.1 Load libraries

# Python libraries
# Classic,data manipulation and linear algebra
import pandas as pd
import numpy as np

# Plots
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import plotly.offline as py
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.tools as tls
import plotly.figure_factory as ff
py.init_notebook_mode(connected=True)
import squarify

# Data processing, metrics and modeling
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.metrics import precision_score, recall_score, confusion_matrix,  roc_curve, precision_recall_curve, accuracy_score, roc_auc_score
import lightgbm as lgbm

# Stats
import scipy.stats as ss
from scipy.stats import randint as sp_randint
from scipy.stats import uniform as sp_uniform

# Time
from contextlib import contextmanager
@contextmanager
def timer(title):
    t0 = time.time()
    yield
    print("{} - done in {:.0f}s".format(title, time.time() - t0))

#ignore warning messages 
import warnings
warnings.filterwarnings('ignore') 

1.2 Read data

# Reading dataset
train = pd.read_csv("../input/train.csv")
test = pd.read_csv("../input/test.csv")

2. Overview

2.1 Head

# Head train and test
display(train.head())
display(test.head())

The rest of this analysis is available in the folowing link: https://bit.ly/2GKjWUA