Data Science and Artificial Intelligence

7Classification Problem Case Study

Case Study of the Titanic Data Set: A Classification Problem Statement using Stratified Cross-Validation

Dataset

https://s3.amazonaws.com/acadgildsite/wordpress_images/datasets/titanic/test.csv

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https://s3.amazonaws.com/acadgildsite/wordpress_images/datasets/titanic/train.csv

Prepare Datasets

import re

import numpy as np

import pandas as pd

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import StratifiedKFold, GridSearchCV, cross_val_score

from sklearn.linear_model import LogisticRegression, LogisticRegressionCV

# read the data sets

# ‘X’ data frame will store train data

# ‘y’ series will store the ‘Survived’ data

# ‘Z’ will store test data

X = pd.read_csv(“train.csv”)

y = X[‘Survived’]

Z = pd.read_csv(“test.csv”)

X_orig = X.copy()

Z_orig = Z.copy()

X.head()

Output:

 

 

 

 

 

 

 

 

 

 

# Check for missing values in ‘Age’, ‘Fare’, ‘Embarked’ and fill them with the median value; do it for both test and train sets

X[‘Age’].fillna(X[‘Age’].median(), inplace=True)

X[‘Embarked’].fillna(‘S’, inplace=True)

Z[‘Age’].fillna(Z[‘Age’].median(), inplace=True)

Z[‘Fare’].fillna(Z[‘Fare’].median(), inplace=True)

# Perform dummy treatment on ‘Pclass’, ‘Sex’, ‘SibSp’, ‘Parch’, ‘Embarked’ for both test and train sets and drop the original columns

X = pd.concat([X, pd.get_dummies(X[‘Pclass’], prefix=”PClass”),

pd.get_dummies(X[‘Sex’], prefix=”Sex”),

pd.get_dummies(X[‘SibSp’], prefix=”SibSp”),

pd.get_dummies(X[‘Parch’], prefix=”Parch”),

pd.get_dummies(X[‘Embarked’], prefix=”Embarked”)], axis=1)

Z = pd.concat([Z, pd.get_dummies(Z[‘Pclass’], prefix=”PClass”),

pd.get_dummies(Z[‘Sex’], prefix=”Sex”),

pd.get_dummies(Z[‘SibSp’], prefix=”SibSp”),

pd.get_dummies(Z[‘Parch’], prefix=”Parch”),

pd.get_dummies(Z[‘Embarked’], prefix=”Embarked”)], axis=1)

X.drop([‘Survived’, ‘Pclass’, ‘Name’, ‘Sex’, ‘SibSp’, ‘Parch’, ‘Ticket’, ‘Cabin’, ‘Embarked’, ‘PassengerId’], axis=1, inplace=True)

Z.drop([‘Pclass’, ‘Name’, ‘Sex’, ‘SibSp’, ‘Parch’, ‘Ticket’, ‘Cabin’, ‘Embarked’, ‘PassengerId’, ‘Parch_9’], axis=1, inplace=True)

# Check data structure

X.head()

Output:

 

 

 

 

 

 

 

 

X.shape

Output: (891, 24)

Z.head()

Output:

 

 

 

 

 

 

Z.shape

Output: (418, 24)

Feature Engineering

## Perform Binning for Age and Fare

# Perform median groups and drop the original columns

X[‘Age_cat’] = pd.qcut(X.Age, q=4, labels=False)

X.drop([‘Age’], axis=1, inplace=True)

Z[‘Age_cat’] = pd.qcut(Z.Age, q=4, labels=False)

Z.drop([‘Age’], axis=1, inplace=True

X[‘Fare_cat’] = pd.qcut(X.Fare, q=4, labels=False)

X.drop([‘Fare’], axis=1, inplace=True)

## Cabin usage: add feature ‘has_cabin’ which will yield cell values of ‘false’ or ‘true’

X[‘has_Cabin’] = ~X_orig.Cabin.isnull()

Z[‘has_Cabin’] = ~Z_orig.Cabin.isnull()

Output:

 

 

 

 

 

 

 

 

 

 

## Extract the Titles of passengers (use lambda function) and do dummy treatment

# Concatenate the dummies with the parent data frame

X_title = pd.DataFrame(index = X_orig.index)

X_title[‘Title’] = X_orig.Name.apply(lambda x: re.search(‘ ([A-Z][a-z]+)\.’, x).group(1))

X_title[‘Title’] = X_title[‘Title’].replace({‘Mlle’:’Miss’, ‘Mme’:’Mrs’, ‘Ms’:’Miss’})

X_title[‘Title’] = X_title[‘Title’].replace([‘Don’, ‘Dona’, ‘Rev’, ‘Dr’,

‘Major’, ‘Lady’, ‘Sir’, ‘Col’, ‘Capt’, ‘Countess’, ‘Jonkheer’],’Special’)

X = pd.concat([X, pd.get_dummies(X_title[‘Title’], prefix=’Title’)], axis=1)

Z_title = pd.DataFrame(index = Z_orig.index)

Z_title[‘Title’] = Z_orig.Name.apply(lambda x: re.search(‘ ([A-Z][a-z]+)\.’, x).group(1))

Z_title[‘Title’] = Z_title[‘Title’].replace({‘Mlle’:’Miss’, ‘Mme’:’Mrs’, ‘Ms’:’Miss’})

Z_title[‘Title’] = Z_title[‘Title’].replace([‘Don’, ‘Dona’, ‘Rev’, ‘Dr’,

‘Major’, ‘Lady’, ‘Sir’, ‘Col’, ‘Capt’, ‘Countess’, ‘Jonkheer’],’Special’)

Z = pd.concat([Z, pd.get_dummies(Z_title[‘Title’], prefix=’Title’)], axis=1)

#Check column names and cell values

X.head()

Output:

 

 

 

 

 

 

 

 

Z.head().T

Output:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Decision Tree

# Set stratified cross validation parameters

skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=5)

# Set the tree parameters

decision_tree_params = {‘max_depth’: list(range(1, 5)),

‘min_samples_leaf’: list(range(1, 5))}

# Set the tree grid

decision_tree_grid = GridSearchCV(DecisionTreeClassifier(random_state=17),

decision_tree_params, verbose=True, n_jobs=-1, cv=skf)

decision_tree_grid.fit(X, y)

# print out the best tree parameters

print(‘Best decision tree params:’, decision_tree_grid.best_params_)

# print out the best tree cross validation score

print(‘Best decision tree cross validation score:’, decision_tree_grid.best_score_)

Output:

Fitting 5 folds for each of 16 candidates, totalling 80 fits

Best decision tree params: {‘max_depth’: 4, ‘min_samples_leaf’: 1}

Best decision tree cross validation score: 0.8170594837261503

decision_tree_predictions = decision_tree_grid.best_estimator_.predict(Z)

Logit

# Set the logit parameters

c_values = np.logspace(-2, 3, 500)

# perform the grid search algo

logit_searcher = LogisticRegressionCV(Cs=c_values, cv=skf, verbose=1, n_jobs=-1)

logit_searcher.fit(X, y)

#print out the best tuning parameter

print(‘Best C:’, logit_searcher.C_)

Output:

Best C: [0.68181623]

#Predict on the test data set

logit_predictions = logit_searcher.predict(Z)

 

prateek

An alumnus of the NIE-Institute Of Technology, Mysore, Prateek is an ardent Data Science enthusiast. He has been working at Acadgild as a Data Engineer for the past 3 years. He is a Subject-matter expert in the field of Big Data, Hadoop ecosystem, and Spark.

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