Titanic Survival Prediction

Introduction

This project analyzes the Titanic dataset to predict passenger survival using machine learning models. The project includes data preprocessing, feature engineering, model training, and evaluation.

Table of Contents

• Introduction
• Installation
• Usage
• Features
• Dependencies
• Configuration
• Documentation
• Examples
• Troubleshooting
• Contributors
• License

Installation

1. Clone the repository:

   git clone https://github.com/Ndrake337/Kagle_Titanic.git
   cd Kagle_Titanic

2. Install the required packages:

   pip install -r requirements.txt

Usage

To run the analysis and prediction:

1. Ensure you have the Titanic dataset (train.csv and test.csv) in the project directory.
2. Open and run the Jupyter notebook:

   jupyter notebook Titanic.ipynb

Features

• Data loading and preprocessing
• Feature engineering
• Model training using RandomForestClassifier and LogisticRegression
• Model evaluation using cross-validation
• Error analysis

Dependencies

• pandas
• numpy
• matplotlib
• scikit-learn

Configuration

Ensure the following configurations in the notebook:

• File paths for train.csv and test.csv.
• Random state and other parameters for reproducibility.

Documentation

Data Loading

train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')

Feature Engineering

Converting categorical 'Sex' feature to binary:

def binarySex(valor):
    if valor == 'female':
        return 1
    else:
        return 0

train['Sex_binario'] = train['Sex'].map(binarySex)
test['Sex_binario'] = test['Sex'].map(binarySex)

Model Training and Evaluation

Using Repeated K-Fold Cross-Validation with RandomForestClassifier:

from sklearn.model_selection import RepeatedKFold
from sklearn.ensemble import RandomForestClassifier

resultados = []
kf = RepeatedKFold(n_splits=2, n_repeats=10, random_state=10)

for linhas_treino, linhas_valid in kf.split(X):
    X_treino, X_valid = X.iloc[linhas_treino], X.iloc[linhas_valid]
    y_treino, y_valid = y.iloc[linhas_treino], y.iloc[linhas_valid]
    
    modelo = RandomForestClassifier(n_estimators = 100, n_jobs=-1, random_state=0)
    modelo.fit(X_treino, y_treino)
    
    p = modelo.predict(X_valid)
    acc = np.mean(y_valid == p)
    resultados.append(acc)

Examples

Analyzing errors in predictions:

X_valid_check = train.iloc[linhas_valid].copy()
X_valid_check['p'] = p
erros = X_valid_check[X_valid_check['Survived'] != X_valid_check['p']]

Troubleshooting

• Ensure all dependencies are installed.
• Verify the paths to the dataset files are correct.
• Check for missing values and handle them appropriately before training.

Contributors

• Your Name - @Ndrake337

License

This project is licensed under the MIT License - see the LICENSE file for details.