Titanic Data Analysis & Visualization

an advanced analysis of the Titanic dataset

Project Overview

Performed feature engineering, create visualizations, and build predictive models to understand the factors that influenced survival rates.

Technologies: Python, Pandas, Seaborn, Plotly, Scikit-learn.

Duration: 1 week

Team Size: 1 members

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Open Github Repository

Dataset

The dataset used for this analysis is the Titanic dataset available from the Seaborn library, which is a cleaned version of the original Titanic dataset from Kaggle.

Features

The following features were used in the analysis:
1. pclass: Passenger class (1st, 2nd, 3rd)
2. age: Age of the passenger
3. sibsp: Number of siblings/spouses aboard
4. parch: Number of parents/children aboard
5. fare: Ticket fare
6. fare_per_person: Fare per person, calculated as fare / (sibsp + parch + 1)
7. is_alone: Binary feature indicating if a passenger is traveling alone

How to Use This Repository

1. Clone the Repository

To get started, clone this repository to your local machine using the following command:

git clone https://github.com/Durveshbaharwal/Advance-Titanic-Data-Analysis-and-Visualization
cd Advance-Titanic-Data-Analysis-and-Visualization

2. Install dependencies:

You can install the required libraries using pip:

pip install pandas seaborn matplotlib plotly scikit-learn lifelines

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