Titanic EDA Project

Project Overview

This project explores the Titanic dataset to analyze survival trends and generate insights. It involves:

1. Loading and inspecting the dataset.
2. Performing exploratory data analysis (EDA).
3. Visualizing survival trends by gender, class, and age.
4. Cleaning and saving the dataset.

Key Steps

1. Load Data: Used pandas to load and preview the Titanic dataset.
2. Check Missing Values: Identified columns with missing data and decided on handling strategies.
3. Univariate Analysis: Visualized age and survival distributions using histograms.
4. Bivariate Analysis: Analyzed survival rates by gender and class using bar plots.
5. Handle Missing Data: Filled missing values in the age column and dropped columns with excessive missing data.
6. Save Cleaned Data: Saved the cleaned dataset for future use.

Key Insights

1. Survival rates were higher for women compared to men.
2. Passengers in higher classes (e.g., first-class) had a greater chance of survival.
3. Age distribution showed that younger passengers had slightly better survival rates.

Files in Repository

• titanic3.xls: Original dataset.
• EDA_Titanic.ipynb: Jupyter Notebook documenting the EDA process.
• cleaned_titanic_data.xlsx: Cleaned dataset.
• README.md: Project overview and methodology.

Requirements

To run the analysis, install the following Python libraries:

pip install pandas matplotlib seaborn xlrd openpyxl