My First Data Science Project: Analyzing Titanic Survival Data with Python

Hi!
Welcome to my very first mini data science project blog!
In this post, I’ll walk you through how I used Python, Pandas, and Seaborn to explore the famous Titanic dataset — a classic beginner-friendly dataset used in data science learning.

---

🚢 What is the Titanic Dataset?

The Titanic dataset contains data about the passengers on board the RMS Titanic — a ship that tragically sank in 1912.
The dataset includes details like:

• Passenger age, gender, class

• Ticket fare

• Whether they survived or not

Our goal is to explore the data and uncover patterns — such as:
"Who had the best chance of survival?"

---

📥 Step 1: Import Libraries and Load Data

We’ll use Pandas and Seaborn for data analysis and visualization.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load Titanic dataset
df = sns.load_dataset('titanic')
df.head()

---

🧾 Step 2: Explore the Data

Let’s check the structure and look for missing values.

df.info()
df.isnull().sum()

Some initial observations:

• Columns like age, embarked, and deck have missing values.

• The survived column is our target — 1 means survived, 0 means not.

---

🧹 Step 3: Data Cleaning

Let’s handle some missing data and keep only useful columns.

# Drop columns with many missing values
df.drop(['deck', 'embark_town', 'alive'], axis=1, inplace=True)

# Fill missing age with median
df['age'].fillna(df['age'].median(), inplace=True)

# Drop rows with any remaining nulls
df.dropna(inplace=True)

---

📊 Step 4: Data Visualization

Let’s find some interesting insights.

1. Survival Count

sns.countplot(x='survived', data=df)
plt.title('Survival Count')
plt.show()

2. Survival by Gender

sns.countplot(x='sex', hue='survived', data=df)
plt.title('Survival by Gender')
plt.show()

3. Survival by Class

sns.countplot(x='pclass', hue='survived', data=df)
plt.title('Survival by Passenger Class')
plt.show()

4. Age Distribution

sns.histplot(data=df, x='age', bins=30, kde=True)
plt.title('Age Distribution of Passengers')
plt.show()

---

📈 Step 5: What Did I Learn?

🔍 Insights:

• More women survived than men.

• First-class passengers had a higher survival rate.

• Younger passengers had better chances.

This small project helped me understand:

• How to load and clean data

• How to find patterns visually

• How real-world data can tell powerful stories!

---

🧠 What’s Next?

In my next blog, I plan to build a basic machine learning model using this Titanic dataset — to actually predict survival!
Step by step, I’ll keep growing my data science skills, and I hope you’ll follow along.

Thanks for reading 💛
Feel free to try this project yourself and share your results!

---

— Farsana | Data Science Intern | Python + Pandas + Curiosity 🚀

---