🚢 Titanic - Machine Learning from Disaster
📌 Overview
This repository contains a structured, end-to-end Machine Learning pipeline developed for the classic Titanic: Machine Learning from Disaster competition on Kaggle. The objective is to analyze historical passenger data and accurately predict survival outcomes using robust data pre-processing and diverse classification algorithms.
🎨 System Architecture Flow
The code is designed with crisp segregation following data science development standard practices:
[Raw Dataset] ➡️ [EDA & Missing Value Imputation] ➡️ [Feature Scaling & Encoding] ➡️ [Multi-Model Evaluation] ➡️ [Cross-Validation Optimization]
📊 Model Performance & Results
We trained and evaluated multiple classification paradigms on the scaled Titanic features. To maintain generalization and prevent data leakage or overfitting, cross-validation scoring was implemented on the top-performing architecture.
| Rank | Machine Learning Classifier | Evaluation Metric | Accuracy Score |
|---|---|---|---|
| 1 | 🏆 Support Vector Machine (SVM) | Cross-Val Mean | 82.79% |
| 2 | 📈 Logistic Regression | Test Accuracy | 80.33% |
| 3 | 🔍 KNN (K-Neighbors Classifier) | Test Accuracy | 79.21% |
| 4 | 🌲 Decision Tree | Test Accuracy | 75.28% |
💡 UI/UX & Developer Insight: While standard linear boundaries like Logistic Regression provided a highly reliable baseline (
80.33%), the non-linear high-dimensional hyperplane of the Support Vector Machine (SVM) optimally captured the variance in features like class, age, and fare—resulting in our most stable performance metric (82.79%).
🚀 Getting Started
1. Installation & Environment Setup
Clone this specific repository setup directly to your machine:
git clone [https://github.com/amirsohail100/Titanic---Machine-Learning-from-Disaster.git](https://github.com/amirsohail100/Titanic---Machine-Learning-from-Disaster.git)
cd Titanic---Machine-Learning-from-Disaster
pip install -r requirements.txt
Senior Developer aur UI/UX Designer ke taur par, maine aapke repository ke table ko aur zyada structural aur visually rich bana diya hai. Saath hi aapka Description Message bhi strictly 350-character limit ke andar update kar diya hai.