In this project, I've worked with the Titanic Data Set from Kaggle. This is a very famous data set and is often a student's first step in machine learning!
The objective of this project is to predict whether a passenger on the Titanic survived or not using a logistic regression model.
I've used a "semi-cleaned" version of the Titanic dataset. If you use the dataset hosted directly on Kaggle, you may need to do some additional cleaning not shown in this lecture notebook.
- PassengerId: Unique ID for each passenger.
- Survived: Survival (0 = No, 1 = Yes).
- Pclass: Ticket class (1 = 1st, 2 = 2nd, 3 = 3rd).
- Name: Name of the passenger.
- Sex: Gender of the passenger.
- Age: Age of the passenger.
- SibSp: Number of siblings/spouses aboard the Titanic.
- Parch: Number of parents/children aboard the Titanic.
- Ticket: Ticket number.
- Fare: Passenger fare.
- Cabin: Cabin number.
- Embarked: Port of Embarkation (C = Cherbourg, Q = Queenstown, S = Southampton).
To run this project, you'll need to install the following libraries:
- pandas
- numpy
- matplotlib
- seaborn
- scikit-learn
You can install these using pip:
pip install pandas numpy matplotlib seaborn scikit-learn- Importing libraries
- Exploratory Data Analysis
- Finding out the missing data attributes
- Cleaning the data
- Converting Categorical features
- Building a Logistic regression Model
- And finally evaluating the results