How to Do a Data Science Project

Simple Data Science Project: Titanic Survival Analysis Step-by-Step

This beginner-friendly Data Science project walks you through the process of analyzing the Titanic dataset to uncover patterns in passenger survival. We'll clean, visualize, and interpret the data using Python, pandas, seaborn, and matplotlib.

Tools Required: Python, Jupyter Notebook or VS Code, pandas, seaborn, matplotlib.

Step 1: Import Required Libraries

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

Step 2: Load the Titanic Dataset

You can use the dataset from Kaggle or seaborn's built-in Titanic data:

df = sns.load_dataset("titanic")
df.head()

Step 3: Understand the Data

Explore the dataset structure:

df.info()
df.describe()

Check for missing values:

df.isnull().sum()

Step 4: Clean the Data

Drop irrelevant columns and fill or remove missing values:

# Drop 'deck' due to many missing values
df = df.drop(columns=['deck'])

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

# Drop rows with missing 'embarked'
df.dropna(subset=['embarked'], inplace=True)

Step 5: Data Visualization

Visualize survival by gender:

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

Visualize survival by class:

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

Step 6: Analyze the Findings

• Women had a higher chance of survival.
• First-class passengers survived more often than third-class.
• Younger passengers (children) had better survival rates.

Step 7: Conclusion

We successfully performed data cleaning, visualization, and basic analysis on the Titanic dataset. This project helps develop core data science skills such as EDA (Exploratory Data Analysis), handling missing values, and plotting insights.

What's Next?

• Try predictive modeling (e.g., Logistic Regression) using scikit-learn.
• Explore other datasets like Iris, Wine Quality, or Movie Ratings.
• Upload your project to GitHub and share with others!