Abstract : 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.In this paper we are
going to make the predictive analysis of
what sorts of people were likely to survive and using some tools of machine learing to predict which passengers survived the
tragedy with accuracy..
– Machine learning .
Machine learning means the application of any
computer-enabled algorithm that can be applied against a data set to find a
pattern in the data. This encompasses
basically all types of data science algorithms, supervised, unsupervised,segmentation,
classification, or regression”.few important areas where machine learning can
be applied are
written letters into digital letters
Language Translation:translate spoken
and or written languages (e.g. Google Translate)
Speech Recognition:convert voice
snippets to text (e.g. Siri, Cortana, and Alexa)ü
Image Classification:label images with
appropriate categories (e.g. Google Photos)
Autonomous Drivin:genable cars to
drive (e.g. NVIDIA and Google Car)
of machine learning algorithms are :
Features are the observations that are used to form predictions
For image classification, the pixels
are the features
For voice recognition, the pitch and
volume of the sound samples are the features
For autonomous cars, data from the
cameras, range sensors, and GPS are features
Extracting relevant features is important for building
Source of mail is an irrelevant feature when
Source is relevant when classifying emails because
SPAM often originates from reported sources
machine learning algorithm works best under a given set of conditions. Making
sure your algorithm fits the assumptions / requirements ensures superior
performance. You can’t use any algorithm in any condition.
Instead, in such situations, you should try using
algorithms such as Logistic Regression, Decision Trees, SVM, Random Forest etc.
why Logistic Regression ?
used to model the probability of an evenet occuring depending on the values of
the independent variables which can be categorical and numerical and to
estimate the probability that an event occurs for a randomly selected onservations
versus the probability that the evecnt does not occur and it is used to predict
the effects of series of varibales on a
binary response variable and it is used to classify observations by estimating
the probability that an observation is in a particular category
Peformance of Logistic
AIC (AkaikeInformation Criteria) –The analogous metric of adjusted R² in logistic
regression is AIC. AIC is the measure of
fit which penalizes model for the number of model
coefficients. Therefore, we always prefer model with minimum AIC
Null Deviance and Residual Deviance –Null Deviance indicates the response predicted
by a model with nothing but an
intercept. Lower the value, better the model. Residual
deviance indicates the response predicted by a model on adding
independent variables. Lower the value, better the
It is nothing but a tabular representation of Actual vs Predicted values.
This helps us to find the accuracy
of the model and avoid overfitting.
is called as pseudo R2. Whenanalyzingdata with a logistic regression, an
equivalent statistic to R-squared does not exist. However, to evaluate the
goodness-of-fit of logistic models, several pseudo R-squareds have been
accuracy=truepostives + true negatives/
(truepostivies+true negatives+false positives+false negatives)
Decision tree is a hierarchical tree structurethat can
be used to divide up a large collection of records into smaller sets of classes by applying a
sequence of simple decision rules. A decision tree model consists of a set of
rules for dividing a large heterogeneous population into smaller, more
homogeneous(mutually exclusive) classes.The attributes of the classes can be
any type of variables from binary, nominal, ordinal, and quantitative values,
while the classes must be qualitative type (categorical or binary, or ordinal).
In short, given a data of attributes together with its classes, a decision tree
produces a sequence of rules (or series of questions) that can be used to
recognize the class.
One rule is applied after another, resulting in a hierarchy
of segments within segments. The hierarchy is called a tree, and each segment
is called a node.With each successive division, the members of the resulting
sets become more and more similar to each other.
Hence, the algorithm used to construct decision tree
is referred to as recursive partitioning
Decision tree applications :
prediction tumor cells as benign or
classify credit card transaction as legitimate or
classify buyers from non -buyers
decision on whether or not to approve
diagnosis of various diseases based on
symptoms and profiles
our approach to solve the problem:
1. collect the raw data need to solve the problem.
2. improt the dataset into the working environment
3.Data preprocessing which
includes data wrangling and feature engineering .
4.explore the data and prepare a model for performing analysis using
machine learing algorithms
5.Evaluate the model and re-iterate till we get satisfactory model
6.Compare the results and select a model which gives a more accurate
the data we collected is
still rawdata which is very likely to
contains mistakes ,missing values and corrupt values. before drawing any
conclusions from the data we need to do some data preprocessing which involves
data wrangling and feature engineering .
data wrangling is the process of cleaning and unify the messy and
complex data sets for easy access and analysis
feature engineering process attempts to create additional
relevant features from existing raw features in the data and to increase the
predictive power of learing algorithms
4 Experimental Analysis and Discussion
a) Data set description:
The original data has been split into two
groups :training dataset(70%) and test dataset(30%).The training
set should be used to build your machine learning models..
set should be used to see how well your model performs on unseen data. For
the test set, we do not provide the ground truth for each passenger. It is your
job to predict these outcomes. For each passenger in the test set, use the
model you trained to predict whether or not they survived the sinking of the
after training with the algorithms , we have to validate our trained
algorithms with test data set and measure the algorithms performance with
godness of fit with confusion matrix for validation. 70% of data as training
data set and 30% as training data set
confusion matrix for decision tree
trained data set test
confusion matrix for logistic regression trained data test
d) Enhancements and reasoning
predicting the survival
rate with others machine learing algorithms like random forests , various Support
Vector machines may improve the accuracy
of prediction for the given data set.
analyses revealed interesting patterns across individual-level features.
Factors such as socioeconomic status, social norms and family composition
appeared to have an impact on likelihood of survival. These conclusions,
however, were derived from findings in the dataThe accuracy of predicting the
survival rate using decision tree algorithm(83.7) is high when compared with
logistic regression(81.3) for a given
types of conclusions
1. The analyses revealed interesting patterns across
individual-level features. Factors such as socioeconomic status, social norms
and family composition appeared to have an impact on likelihood of survival.
These conclusions, however, were derived from findings in the data. Many
stories and oral histories have
been collected by both survivors and relatives of the passengers in the past
century, and these qualitative data sets may help to elucidate what really
happened that fateful night.