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Titanic Survival Predictor

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!

Project Description

The objective of this project is to predict whether a passenger on the Titanic survived or not using a logistic regression model.

Dataset

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.

Features of the Dataset

  • 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).

Installation

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

Methodology

  • 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