🎯 Titanic Survival Prediction - Kaggle Competition

This project is a submission for the famous Titanic - Machine Learning from Disaster competition on Kaggle.
The goal is to build a classification model that predicts which passengers survived the Titanic shipwreck.

📁 Dataset Description

The data consists of information such as:

• Pclass (Ticket class)
• Sex
• Age
• SibSp (Number of siblings/spouses aboard)
• Parch (Number of parents/children aboard)
• Fare
• Embarked (Port of embarkation)

The data files used:

• train.csv – Training set
• test.csv – Test set
• gender_submission.csv – Sample submission for test labels

🧹 Data Preprocessing

• Missing values in Age were filled using the mean of the column.
• Missing values in Embarked were filled using the mode.
• The test set was also filled with the mean of training features for simplicity.
• Categorical features Sex and Embarked were encoded using LabelEncoder.

🧠 Models Used

• ✅ XGBoostClassifier – Main model
• (Other models like RandomForestClassifier and LogisticRegression can be tried optionally.)

🧪 Evaluation

Metrics used for evaluation:

• Accuracy: 0.82
• F1-Score: 0.73

Evaluation is based on the labels provided in gender_submission.csv.

🔧 Hyperparameter Tuning

Used GridSearchCV for hyperparameter optimization with the following grid:

param_grid = {
    'max_depth': [3, 5, 7],
    'learning_rate': [0.01, 0.1, 0.2],
    'n_estimators': [100, 200, 300],
    'subsample': [0.8, 1.0],
    'colsample_bytree': [0.8, 1.0]
}

Tuning was done using f1 score with 5-fold cross-validation.

💡 How to Run

Make sure you have the required libraries:

pip install pandas matplotlib scikit-learn xgboost

Then just run the notebook in a Jupyter environment or VSCode, and you're good to go!

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📌 Notes

• This project is for educational purposes only and is part of a Kaggle competition.
• The dataset is publicly available from Kaggle here.
• You can improve the results by performing deeper feature engineering and using ensemble models.