This project focuses on conducting data cleaning and exploratory data analysis (EDA) on the Titanic dataset.The Titanic dataset is a well-known dataset that provides insights into the demographics and survival rates of passengers aboard the Titanic.
The datasets is obtained from Kaggle: Titanic
The dataset contains 891 rows (entries) and 12 columns
The columns are:
PassengerId: Unique identifier for each passenger.
Survived: Binary variable indicating survival (1 = Survived, 0 = Did Not Survive).
Pclass: Ticket class (1st, 2nd, 3rd class).
Name: Passenger's name.
Sex: Gender of the passenger.
Age: Age of the passenger.
SibSp: Number of siblings/spouses aboard.
Parch: Number of parents/children aboard.
Ticket: Ticket number.
Fare: Passenger fare.
Cabin: Cabin number.
Embarked: Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)
Univariate analysis is a data analysis technique that focuses on examining and describing the characteristics and distribution of a single variable in a dataset. In this project, I performed univariate analysis on various variables in the Titanic dataset, including:
- Histograms for age distribution.
- Bar plots for passenger class distribution.
- Pie chart for survival rate.
Bivariate analysis explores relationships between two variables in the dataset. In this project,I investigate relationships such as:
- Scatter plots for age vs. fare.
- Bar plots comparing the number of survivors by passenger class.
- Box plots comparing fares by passenger class.
- Correlation heatmap between age and fare
Multivariate analysis extends the exploration to multiple variables simultaneously.I used techniques like parallel coordinates plots to visualize relationships between multiple variables and survival.
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$59.4$ % of people did not survive while$40.6$ % percent survived. - There is a relatively smaller number of children (around 5-15 years old) and elderly passengers (above 60 years old) on the Titanic
- Passengers in Class 1 had a higher chance of survival compared to those in Class 2 and Class 3. This suggests that the passenger class might have influenced the survival rate
- Class 3 had the highest number of passengers but the lowest survival rate, indicating a potential class-based hierarchy in rescue efforts
- Survived and Fare: There is a positive correlation between "Survived" and "Fare," suggesting that passengers who paid higher fares had a higher chance of survival.
- Age vs. Survived: there might be a slight concentration of younger survivors.