🚢 Titanic Survival Prediction – Classification Case Study

Apply classification algorithms to predict whether a passenger survived the Titanic disaster using real Kaggle dataset features like Age, Gender, Fare, and Passenger Class.

📂 Dataset Overview

Titanic dataset contains details for 891 passengers. Key columns include:

• Survived – target (0 = No, 1 = Yes)
• Pclass – passenger class (1, 2, 3)
• Sex – male/female
• Age – in years
• Fare – ticket price

Python Implementation – Logistic Regression

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix

# Load data
df = pd.read_csv("titanic.csv")

# Handle missing values
df['Age'].fillna(df['Age'].median(), inplace=True)

# Encode categorical variables
df['Sex'] = df['Sex'].map({'male':0, 'female':1})

# Select features
X = df[['Pclass', 'Sex', 'Age', 'Fare']]
y = df['Survived']

# Split dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)

# Predict & evaluate
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))

💡 Tips for Improvement

• Try Decision Trees or Random Forest for better accuracy
• Use feature scaling for numeric values
• Experiment with interaction terms (e.g., Age × Pclass)

📝 Self-Check

1. Why is it important to handle missing Age values?
2. What encoding method was used for Sex?
3. How can model performance be improved?