Overview
The Titanic dataset is one of the most famous beginner-friendly datasets on Kaggle. This project involves building a classification model to predict which passengers survived the Titanic disaster based on features like age, class, gender, and more.
This task will help you practice everything you’ve learned so far—data cleaning, feature engineering, model selection, and evaluation.
Key Concepts
- Binary classification
- EDA on real-world data
- Handling missing values
- Feature engineering with categorical/numerical data
- Model evaluation using accuracy, precision, recall, and F1-score
Practice Exercise
Exercise: Build a Titanic Survival Prediction Model
- Explore the dataset with Pandas and visualize key features (e.g., age distribution, gender impact)
- Clean missing values and encode categorical variables
- Engineer new features (e.g., family size, title extraction from names)
- Train models (Logistic Regression, Decision Tree, Random Forest)
- Evaluate models using cross-validation and confusion matrix
- Submit your best model on Kaggle and compare with the leaderboard
Resources
Kaggle Titanic Competition
Main resource for today
Titanic Survival Prediction Walkthrough
End-to-end ML pipeline on the Titanic dataset
Feature Engineering Tips for Titanic
Ideas to boost your model's performance
Kaggle Getting Started with Titanic
Perfect for beginners to step into competitions
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