Titanic survival prediction implementation using logistic regression algorithm machine learning.

1. TITANIC SURVIVOR
PREDICTION
USING LOGISTIC REGRESSION
Submitted to:
GLA University , Mathura
Presented By:
Ankur Omar
171500051

2. INTRODUCTION
• The sinking of the RMS Titanic is one of the most infamous
shipwrecks in history.
• 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.

3. GOALS AND OBJECTIVE
• The purpose of this project is to document the process I
went through to create my predictions for Titanic
Survivor Prediction.
• The objective of this project was to build a classification
model that could successfully determine whether a
Titanic passenger lived or died.

4. SOFTWARE REQUIRED
• TOOLS USED
1. Anaconda Navigator- 1.9.6
2. Jupyter Notebook- 5.7.4
• LIBRARY USED
1. Analyzing: Numpy, Pandas , Sci-kit Learn
2. Visualization : Matplotlib , Seaborn

5. LOGISTIC REGRESSION
• Logistic regression is basically a supervised classification
algorithm. In a classification problem, the target variable(or
output), y, can take only discrete values for given set of
features(or inputs), X.
• Some of the examples of classification problems are Email
spam or not spam, Online transactions Fraud or not Fraud.

6. IMPLEMENTATION
• Importing the necessary libraries
• Importing the dataset
• Cleaning and analyzing the dataset
• Building the model
• Using logistic regression for making prediction

7. IMPORTING THE NECESSARY LIBRARIES

8. Read And Explore The Data
Training Set: 891 Rows
Testing Set: 418 Rows

9. CLEANING AND ANALYSING THE
DATA

10. Seeing How Independent Variables Are Effecting
Survival
Sex Feature:
DATA VISUALIZATION

11. Siblings or Spouses Feature

12. PClass :Ticket Class

13. Parch Feature:Number of parents of children

14. Age Feature:
sort the age into categorical categories

15. Age Group:

16. Building The Model
Train Set: 80% Test/Cross Validation
Set:20%

17. Making Predictions and
Calculating Accuracy:

18. Results
0 : DEAD
1 : SURVIVE

19. CONCLUSION
• I have removed variables like “Passenger Id” ,
“Name”, ”Ticket” , “Fare” , “Cabin”, as they are not
effecting the target variable much.
• Women, children, and first class passengers as well
as people with a small family had a better chance
at survival. The Embarked doesn’t seem to have an
effect as the percentages are in line with the
amount of people embarked from each port
• And I am getting an accuracy of 77.09%.

20. REFERENCES
• Analyzing Titanic disaster using machine learning
algorithms -Computing, Communication and
Automation (ICCCA), 2017 International Conference
on 21 December 2017, IEEE.
• Eric Lam, Chongxuan Tang, "Titanic Machine
Learning From Disaster", LamTangTitanic Machine
Learning From Disaster, 2012.
• A.NG CS229 Notes, Stanford University ,2012

21. THANK YOU : )