https://github.com/samuelson777/titanic-dataset-analysis
Exploratory data analysis of the Titanic dataset, uncovering insights on passenger survival rates based on gender, age, and class. Includes data cleaning, visualization, and findings.
https://github.com/samuelson777/titanic-dataset-analysis
data-analysis data-visualization exploratory-data-analysis kaggle machine-learning matplotlib pandas python seaborn titanic-dataset
Last synced: 6 months ago
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Exploratory data analysis of the Titanic dataset, uncovering insights on passenger survival rates based on gender, age, and class. Includes data cleaning, visualization, and findings.
- Host: GitHub
- URL: https://github.com/samuelson777/titanic-dataset-analysis
- Owner: Samuelson777
- Created: 2025-04-15T15:15:06.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-04-15T15:27:11.000Z (6 months ago)
- Last Synced: 2025-04-15T16:34:13.291Z (6 months ago)
- Topics: data-analysis, data-visualization, exploratory-data-analysis, kaggle, machine-learning, matplotlib, pandas, python, seaborn, titanic-dataset
- Language: Jupyter Notebook
- Homepage:
- Size: 118 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Titanic Dataset Analysis
## Overview
This project involves exploratory data analysis (EDA) of the Titanic dataset to uncover insights about the passengers and their survival rates. The dataset contains information about the passengers aboard the Titanic, including their age, sex, class, fare, and whether they survived.
## Dataset
The dataset used for this analysis is the Titanic dataset, which can be found on [Kaggle](https://www.kaggle.com/c/titanic/data). The primary file used in this project is `train.csv`.
## Objectives
- Perform data cleaning and preprocessing.
- Conduct exploratory data analysis (EDA) to visualize and understand the data.
- Analyze survival rates based on various factors such as gender, age, and class.
## Technologies Used
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
## Analysis Steps
- Data Loading: Load the Titanic dataset using Pandas.
- Data Exploration: Explore the dataset to understand its structure and identify missing values.
- Data Cleaning: Handle missing values and convert categorical variables into numerical formats.
- Data Visualization: Create visualizations to analyze survival rates based on gender, age, and class.
- Insights: Summarize findings and insights from the analysis.
## Findings
- Females had a higher survival rate compared to males.
- Younger passengers had a higher chance of survival than older passengers.
- Passengers in higher classes (1st class) had a better survival rate than those in lower classes (2nd and 3rd class).
## Conclusion
This project demonstrates the process of exploratory data analysis using the Titanic dataset. The insights gained can help in understanding the factors that influenced survival during the Titanic disaster.
## Acknowledgments
- Kaggle for providing the Titanic dataset.
- The data science community for their resources and tutorials that helped in completing this project.