https://github.com/arnabushna24/titanic-disaster-analysis

Titanic - Machine Learning from Disaster
https://github.com/arnabushna24/titanic-disaster-analysis

data-analysis data-visualization python statistical-analysis

Last synced: 9 months ago
JSON representation

Titanic - Machine Learning from Disaster

• Host: GitHub
• URL: https://github.com/arnabushna24/titanic-disaster-analysis
• Owner: ArnabUshna24
• Created: 2025-05-09T19:30:02.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-05-11T05:39:54.000Z (about 1 year ago)
• Last Synced: 2025-05-19T20:19:36.476Z (about 1 year ago)
• Topics: data-analysis, data-visualization, python, statistical-analysis
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/c/titanic/data
• Size: 284 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Titanic Disaster Analysis

## Overview

This project aims to analyze [Titanic - Machine Learning from Disaster](https://www.kaggle.com/c/titanic/data) dataset and find insights using statistical analysis. It serves the following concerns:

* Retriveing the data from the target location.

* Handling the missing values and outliers (if there is any).

* Performing data visualization.

* Performing basis statistical analyses.

## Data Retrieval

`Titanic - Machine Learning from Disaster` dataset is available on Kaggle. It contains three (3) `.csv` files - `gender_submission.csv`, `test.csv`, and `train.csv`. Among them, `train.csv` file was used for this project. It contains twelve (12) columns - `PassengerId`,	`Survived`,	`Pclass`,	`Name`,	`Sex`,	`Age`,	`SibSp`, 	`Parch`, `Ticket`, `Fare`, `Cabin`, and	`Embarked`. `train.csv` file was loaded into a `pandas` dataframe for further analysis.

## Data Cleaning and Manipulation

To find missing values in the dataset, `isnull` function was used. There were 177 missing `Age` values, 687 missing `Cabin` values, and 2 `Embarked` values. For the missing values in `Age` column, it was imputed with the median of the column values, whereas `Cabin` and `Embarked` columns were handled using `notnull` function and mode, respectively. After that, outlier identification was performed and outliers were then capped. However, there were no duplicated records.

## Data Visualizations

  

  

    

    

  

  

  

     Fig. 1: Distribution of Passengers by Gender 

     Fig. 2: Age Distribution Histogram 

  

  

  

    

    

  

  

   

     Fig. 3: Survival Rate by Gender 

     Fig. 4: Survival Rate by Class 

  

  

## Statistical Analysis

Table 1: Mean, Median and Mode of 'Fare' and 'Age' Columns 


  

     Columns 

     Mean 

     Median 

     Mode 

  

  

     Fare 

     32.2042 

     14.4542 

     8.05 

  

  

     Age 

     29.3616 

     28.0 

     28.0 

  

Table 2: Gender-wise Survival Rate 


  

     Test Component 

     Result 

  

  

  

     Null hypothesis 

     Significant difference in survival rates 

  

  

     Significance level (α) 

     0.05 

  

  

     T-statistic 

     -18.672 

  

  

     P-value 

     2.28 × 10⁻⁶¹ 

  

  

     Decision 

     Reject the null hypothesis 

  

   

     Interpretation 

     There is a significant difference in survival rates between males and females on the Titanic 

  

## Build from Source

Instructions are provided in the `.ipynb` file.

If you have any queries, contact me: arnabnushna24@gmail.com