Titanic Survival Classification 🚢

A machine learning project that predicts passenger survival on the Titanic using the Titanic dataset. The project involves data preprocessing, feature engineering, and hyperparameter-tuned K-Nearest Neighbors (KNN) classification.

Project Structure

titanic/
├── main.py
├── Titanic-Dataset.csv
└── README.md

Overview

This project uses a supervised machine learning approach to classify passengers based on their survival status (Survived = 0 or 1). The key steps involved include:

• Handling missing values
• Encoding categorical data
• Feature engineering (e.g., FamilySize, IsAlone, FareBin, AgeBin)
• Data normalization using MinMaxScaler
• Hyperparameter tuning with GridSearchCV
• Classification using K-Nearest Neighbors

How to Run

1. Ensure you have Python 3 and pip installed.

2. Install required libraries:

   pip install pandas numpy scikit-learn matplotlib seaborn
   

3. Run the script:

   python main.py
   

Model Performance

• Classifier Used: K-Nearest Neighbors (tuned with GridSearchCV)
• Accuracy: 80.72%

Classification Report

Class	Precision	Recall	F1 Score	Support
0 (Did Not Survive)	0.81	0.88	0.85	134
1 (Survived)	0.79	0.70	0.74	89

Confusion Matrix

	Predicted: 0	Predicted: 1
Actual: 0	118	16
Actual: 1	27	62

Or view it visually:

Notes

• The dataset is sourced from Kaggle Titanic Dataset.
• You can modify the model or preprocessing logic in main.py.

Author

debb-major 💙

Tutorial(YouTube) - Code with Josh.