🚢 Titanic Survival Insights – Exploratory Data Analysis (EDA) | Internship Task 5

📍 Context

This repository is part of my Data Analyst Internship with a focus on applying real-world analytical techniques to classic datasets.
Task 5 was to perform a detailed Exploratory Data Analysis (EDA) using the Titanic dataset to uncover patterns and insights that influenced passenger survival.

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🧠 Problem Statement

"What factors contributed to a passenger's likelihood of survival on the Titanic?"

The Titanic dataset is not just about numbers—it's a historic tragedy with real people, and behind every row is a story. This EDA aims to dive into those stories, using data to explore age, gender, class, embarkation, and other factors that played a role in who lived and who didn’t.

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🧰 Tools & Technologies Used

Tool	Purpose
Python	Core analysis
Pandas	Data manipulation
NumPy	Numeric operations
Matplotlib & Seaborn	Data visualization
Google Colab	Cloud-based coding
Markdown & GitHub	Documentation & versioning

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📂 Project Structure

│ ├── Titanic.ipynb # Main EDA Notebook ├── Titanic Dataset.csv # Dataset used for analysis ├── titanic.py #Contain the python code used for the process ├── Exploratory Data Analysis on Titanic Dataset.pdf # PDF report of findings └── README.md # Project documentation

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📊 What I Did – Step-by-Step Walkthrough

1. 📥 Data Loading & Initial Exploration

• Loaded data and explored structure, shape, and summary stats
• Understood feature descriptions and distribution of target variable Survived

2. 🧹 Data Cleaning & Preprocessing

• Identified and handled missing values:
  • Filled Embarked using mode
  • Dropped Cabin due to excessive nulls
  • Imputed Age using median
• Converted categorical data to numeric where necessary

3. 🔍 Univariate Analysis

• Studied distributions of single features like Age, Sex, Fare, and Pclass
• Used histograms, bar plots, and KDE plots

4. 🧩 Bivariate & Multivariate Analysis

• Explored relationships between multiple variables and survival
• Key Findings:
  • Women had a higher survival rate than men
  • Higher class passengers (Pclass = 1) had better survival odds
  • Children (<10 years) also had higher survival chances

5. 💡 Key Insights

Factor	Influence on Survival
Gender	Female passengers had significantly higher survival
Class	First-class passengers had a survival advantage
Age	Children and young adults had a slightly better chance
Embarked	Port 'C' (Cherbourg) had more survivors proportionally
Family Size	Traveling with 1–3 relatives increased chances of survival

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📌 Internship Reflection

✅ Applied critical thinking to historical data
✅ Strengthened real-world EDA skills under internship pressure
✅ Learned the importance of storytelling through visuals and insights
✅ Improved speed and structure for data cleaning and visualization tasks

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🌐 Live Preview

🔗 Notebook on Google Colab:
https://colab.research.google.com/drive/1ca5TwofubXd_vSNIzdJ_6Bh9nXdn1EJy#scrollTo=Cqg7M3LsxEEQ

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🔮 Future Work – Machine Learning Extension

As a next step, I plan to apply classification models (like Logistic Regression or Random Forest) to predict survival using this dataset. This EDA project has helped me understand key features, which will directly feed into the ML model development. I'm actively learning supervised ML techniques to build on this foundation.

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📞 Let's connect

• 📧 Email ID : sirishadsirishad6@gmail.com
• 💼 LinkedIn : https://www.linkedin.com/in/sirisha-d-064b69278/