🚢 Titanic Survival Classification

Predict survival outcomes on the Titanic using machine learning (XGBoost) and the Kaggle dataset.

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📁 Dataset

• train.csv – Training data with survival labels
• test.csv – Test data without survival labels (Download both from Kaggle Titanic Challenge)

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🔧 Requirements

Install required packages:

pip install pandas numpy scikit-learn xgboost

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📂 Project Structure

📆 titanic-survival-classification/
├── Titanic Survival Classification.ipynb
├── train.csv
├── test.csv
└── submission.csv (generated)

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🚀 How to Run

1. Clone this repo:

   git clone https://github.com/your-username/titanic-survival-classification.git
   cd titanic-survival-classification

2. Add train.csv and test.csv to the folder (from Kaggle).

3. Launch the notebook:

   jupyter notebook "Titanic Survival Classification.ipynb"

4. Run all cells to preprocess data, train the model, and generate submission.csv.

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🧐 Methodology

• Preprocessing: Handle missing values, drop irrelevant columns, encode categoricals
• Feature Engineering: Title, FamilySize, IsAlone
• Model: XGBoost with train-validation split
• Output: Predictions saved to submission.csv

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📊 Result

• Validation Accuracy: ~80.45%
• Submission file ready for Kaggle evaluation

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