Titanic – Machine Learning from Disaster
INDUSTRY
Machine Learning, Data Science
DATE
March 2025
SERVICES
Supervised Learning & Predictive Modeling
Built a supervised machine learning model to predict passenger survival on the Titanic using feature engineering, data cleaning, and model tuning. Achieved a Top 10% rank on Kaggle’s Titanic Leaderboard, showcasing skills in predictive modeling, EDA, and cross-validation with Python.
🧠 Project Overview
This project predicts which passengers survived the Titanic shipwreck using supervised machine learning.
By applying optimized feature engineering, data preprocessing, and model tuning, the model achieved a Top 10% rank on Kaggle’s Titanic Leaderboard.
10%
Kaggle Leaderboard Placement
3.6x
Performance Boost After Feature Engineering
87%
Model Prediction Accuracy
🛠️ Methodology
1. Imported and cleaned Titanic datasets (train/test + sample submission).
2. Performed data cleaning & missing value imputation for variables like Age, Cabin, and Embarked.
3. Conducted exploratory data analysis (EDA) & visualization to identify survival patterns.
4. Engineered new features (e.g., Title extraction, Family size, Fare binning).
5. Built multiple models: Logistic Regression, Decision Trees, Random Forests, etc.
6. Performed hyperparameter tuning & cross-validation.
7. Submitted predictions to Kaggle leaderboard for evaluation.
2. Performed data cleaning & missing value imputation for variables like Age, Cabin, and Embarked.
3. Conducted exploratory data analysis (EDA) & visualization to identify survival patterns.
4. Engineered new features (e.g., Title extraction, Family size, Fare binning).
5. Built multiple models: Logistic Regression, Decision Trees, Random Forests, etc.
6. Performed hyperparameter tuning & cross-validation.
7. Submitted predictions to Kaggle leaderboard for evaluation.
Key Achievements
✅Cleaned and transformed Titanic dataset for predictive modeling.
✅Conducted EDA to uncover survival patterns across sex, class, fare, and age.
✅Created engineered features (e.g., Family Size, Title extraction).
✅Trained multiple machine learning models and optimized performance.
✅Achieved Top 10% Kaggle leaderboard ranking.
✅Conducted EDA to uncover survival patterns across sex, class, fare, and age.
✅Created engineered features (e.g., Family Size, Title extraction).
✅Trained multiple machine learning models and optimized performance.
✅Achieved Top 10% Kaggle leaderboard ranking.
