George9884/titanic-survival-analysis-python
Type de record:
project
Créateur:
Geo
Hôte:
This project explores the famous Titanic dataset using Python and Pandas. The objective was to perform Exploratory Data Analysis (EDA) to uncover patterns and factors that influenced passenger survival during the Titanic disaster. This project was completed as part of the Data Science Nigeria (DSN) Data Science and AI learning program.
# # Titanic Survival Analysis: Exploratory Data Analysis with Pandas
## Project Overview
This project performs Exploratory Data Analysis (EDA) on the famous Titanic dataset using Python and Pandas.
The objective is to explore passenger information, understand the structure of the dataset, identify missing values, and uncover factors that influenced survival rates during the Titanic disaster.
This project was completed as part of the Data Science Nigeria (DSN) 2026 Free AI Classes Program.
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## Problem Statement
The Titanic dataset contains passenger information such as:
- Passenger Class
- Gender
- Age
- Fare
- Cabin Information
- Embarkation Port
- Survival Status
The goal of this analysis is to investigate patterns in the data and determine which factors were most strongly associated with passenger survival.
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## Technologies Used
- Python
- Pandas
- Google Colab
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## Dataset Source
Titanic Dataset
Source:
raw.githubusercontent.com
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## Project Tasks
### Task 1: Load the Dataset
The Titanic dataset was imported directly from GitHub using Pandas.
```python
import pandas as pd
url = "raw.githubusercontent.com"
df = pd.read_csv(url)
print(df.head())
Task 2: Perform Exploratory Data Analysis (EDA)
The dataset was explored to understand:
Dataset dimensions
Column names
Data types
Missing values
Statistical summaries
Key functions used:
Python
df.shape
df.info()
df.describe()
df.isnull().sum()
Survival Analysis
Overall Survival Rate
The overall survival rate was calculated using:
Python
df['Survived'].mean()
Result:
Plain text
38.38%
This means approximately 4 out of every 10 passengers survived.
Survival Rate by Gender
Python
df.groupby('Sex')['Survived'].mean()
Results:
Gender
Survival Rate
Female
74.20%
Male
18.89%
Key Insight
Female passengers were significantly more likely to survive than male passengers.
This suggests that gend …