🚢 Titanic EDA Analytics

An interactive Titanic Exploratory Data Analysis (EDA) website built using Python, Flask, Pandas, and Plotly.

The project explores the Titanic passenger dataset and presents important survival patterns through an interactive web dashboard.

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📌 Project Overview

The Titanic EDA Analytics project analyzes passenger information to understand the factors associated with passenger survival.

The dataset contains information such as:

• Passenger class
• Gender
• Age
• Fare
• Number of siblings/spouses
• Number of parents/children
• Embarkation point
• Survival status

The analysis is presented through an interactive Flask web application with Plotly visualizations.

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🎯 Objectives

The main objectives of this project are:

• Analyze the Titanic passenger dataset.
• Understand overall survival patterns.
• Compare survival between male and female passengers.
• Analyze survival across different passenger classes.
• Study passenger age distribution.
• Explore fare distribution.
• Present EDA results through an interactive website.
• Create a simple and professional analytics dashboard.

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🛠️ Technologies Used

Technology	Purpose
🐍 Python	Programming language
🌐 Flask	Web application framework
🐼 Pandas	Data processing and analysis
📊 Plotly	Interactive data visualization
HTML	Website structure
CSS	Website styling
Jupyter Notebook	Exploratory data analysis

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📊 Dashboard Features

The web dashboard currently provides:

👥 Dataset Overview

Displays important summary statistics:

• 891 Total Passengers
• 342 Survivors
• 549 Did Not Survive
• 38.38% Survival Rate

📈 Interactive Visualizations

The dashboard includes:

1. Survival Distribution

   • Compares passengers who survived and did not survive.

2. Survival by Gender

   • Compares survival outcomes between male and female passengers.

3. Survival by Passenger Class

   • Shows survival patterns across first, second, and third class.

4. Age Distribution

   • Displays the distribution of passenger ages.

5. Fare Distribution

   • Shows the distribution of passenger fares.

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🔍 Key Analysis Areas

The project focuses on understanding relationships between survival and:

• Gender
• Passenger class
• Age
• Fare
• Family-related information
• Embarkation point

These variables help identify patterns in the Titanic passenger data.

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📁 Project Structure

Titanic-EDA/
│
├── app.py
│
├── train.csv
│
├── Titanic-EDA (2).ipynb
│
├── templates/
│   └── index.html
│
├── static/
│   └── style.css
│
└── README.md