Title : Titanic - Machine Learning from Disaster

Description:

The objective of this code is to build a predictive model using XGBoostClassifier that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name,age,gender,socio-economic class,etc).

Load titanic dataset

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

Read titanic train dataset and store it in train_data

train_data = pd.read_csv("./titanic/train.csv")
train_data.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

Read titanic test dataset and store it in test_data

test_data = pd.read_csv("./titanic/test.csv")
test_data.head()

	PassengerId	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	892	3	Kelly, Mr. James	male	34.5	0	0	330911	7.8292	NaN	Q
1	893	3	Wilkes, Mrs. James (Ellen Needs)	female	47.0	1	0	363272	7.0000	NaN	S
2	894	2	Myles, Mr. Thomas Francis	male	62.0	0	0	240276	9.6875	NaN	Q
3	895	3	Wirz, Mr. Albert	male	27.0	0	0	315154	8.6625	NaN	S
4	896	3	Hirvonen, Mrs. Alexander (Helga E Lindqvist)	female	22.0	1	1	3101298	12.2875	NaN	S

Steps to follow

step1 : Using "Pclass","Sex","SibSp","Age","Fare","Embarked" features to build a model
step2 : Replacing the string values with numerics of 'Sex' feature in train_data
step3 : Replacing the string values with numerics of 'Embarked' feature in train_data
step4 : Extracting the above columns from train_data store it in X
step5 : Extract the class('Survived') column from train_data and store it in y
step6 : repeat steps from 2 to 4 for test_data
step7 : Using XGBoostClassifier with finely tuned hyperparameters to build titanic disaster predictive model 
step8 : Fit the model using X and y
step9 : predict the test_y for X_test using the fitted model

import numpy as np
import xgboost as xgb

lsfeatures = ["Pclass","Sex","SibSp","Age","Fare","Embarked"]

train_data['Sex'].replace(['male','female'],[0,1], inplace = True)
train_data['Embarked'].replace(['S','C','Q'],[0,1,2], inplace = True)
X = train_data[lsfeatures]
Y = train_data['Survived']

test_data['Sex'].replace(['male','female'],[0,1], inplace = True)
test_data['Embarked'].replace(['S','C','Q'],[0,1,2], inplace = True)
X_test = test_data[lsfeatures]


model = xgb.XGBClassifier(n_estimators = 30, 
                      learning_rate=0.1, 
                      max_depth= 10,
                      colsample_bytree =0.9,
                      use_label_encoder=False, random_state =20)
model.fit(X, Y)
predictions = model.predict(X_test)

output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
output.to_csv('submission.csv', index=False)
print("Your submission was successfully saved!")

Your submission was successfully saved!