Introduction
The Titanic dataset from Kaggle includes information on the passengers of the Titanic, aiming to predict survival based on various features. The dataset headers are as follows: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, and Embarked. The purpose of this analysis is to derive initial insights that can guide further data analysis and predictive modeling. https://hng.tech/internship and https://hng.tech/hire
Observations
Summary Statistics
- Total Passengers: 891
- Survival Rate:
- Survived: 38.4%
- Did not survive: 61.6%
Class Distribution:
- 1st Class: 24.2%
- 2nd Class: 20.7%
- 3rd Class: 55.1%
Gender Distribution:
- Male: 64.8%
- Female: 35.2%
Initial Insights
- Survival by Gender:
- Males: 18.9% survived
- Females: 74.2% survived
Survival by Class:
- 1st Class: 62.9% survived
- 2nd Class: 47.3% survived
- 3rd Class: 24.2% survived
Age Distribution:
- Average Age: 29.7 years
- Age Range: 0.42 to 80 years
Fare Distribution:
- Average Fare: $32.20
- Fare Range: $0 to $512.33
Basic Visualization
- Survival by Gender:
- Survival by Class:
Conclusion
The initial analysis indicates significant differences in survival rates based on gender and class. Females and first-class passengers had a much higher survival rate compared to males and third-class passengers. Further analysis should focus on:
- Correlation Analysis: Investigating relationships between survival and features like
Age
,Fare
,SibSp
, andParch
. - Predictive Modeling: Using classification algorithms to predict survival outcomes.
- Handling Missing Data: Addressing missing values in
Age
,Cabin
, andEmbarked
columns for more comprehensive analysis.