📊 CodeAlpha Task 2 - Exploratory Data Analysis (EDA)

Project Overview

This project was completed as part of the CodeAlpha Python Programming Internship.

The objective of this project is to perform Exploratory Data Analysis (EDA) on the Titanic dataset to discover patterns, identify missing values, analyze relationships between variables, and present findings through statistical summaries and visualizations.

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Dataset

Dataset Used: Titanic Dataset

The dataset contains passenger information such as:

• Passenger ID
• Survival Status
• Passenger Class
• Name
• Gender
• Age
• Fare
• Cabin
• Embarked Port

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Technologies Used

• Python
• Google Colab
• Pandas
• NumPy
• Matplotlib

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Exploratory Data Analysis Performed

• Dataset Overview
• Dataset Information
• Missing Value Analysis
• Statistical Summary
• Survival Count Analysis
• Survival by Gender
• Age Distribution
• Survival by Passenger Class
• Fare Distribution
• Correlation Matrix
• Correlation Heatmap

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Key Insights

• Female passengers had a higher survival count than male passengers.
• First Class passengers had better survival rates.
• Most passengers were between 20 and 40 years old.
• Higher fares were generally associated with First Class passengers.
• Some columns, such as Age and Cabin, contain missing values.
• Correlation analysis revealed relationships between passenger class, fare, and survival.

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Files Included

• CodeAlpha_Task2_EDA.ipynb
• titanic.csv
• README.md

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How to Run

1. Open the notebook in Google Colab or Jupyter Notebook.
2. Install the required libraries if needed:

pip install pandas numpy matplotlib

3. Upload titanic.csv.
4. Run all notebook cells.

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Author

Pavithra M

GitHub: https://github.com/PavithraM-prog

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Internship

Submitted as Task 2 for the CodeAlpha Python Programming Internship.