Titanic - Random Forest (v1)

This repository contains my solution to the classic Kaggle competition: Titanic - Machine Learning from Disaster. The goal is to predict which passengers survived the Titanic shipwreck using a classification model.

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📊 Overview

• Competition: Titanic - Machine Learning from Disaster
• Model: Random Forest Classifier
• Public Score: 0.76076
• Best Score: 0.76076 (Version 1)

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📁 Dataset

The dataset includes passenger details such as age, gender, ticket class, number of siblings/spouses aboard, and fare. These features were used to build the model.

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🧹 Data Preprocessing

The following preprocessing steps were applied:

• Dropped unnecessary columns: PassengerId, Name, Ticket, Cabin
• Filled missing values:
  • Age: Filled with median
  • Embarked: Filled with mode ('S')
  • Fare: Filled with median (only in test set)
• Converted categorical variables:
  • Sex: Binary mapping
  • Embarked: One-Hot Encoding

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🤖 Model

• Algorithm: RandomForestClassifier from sklearn.ensemble
• Training-Validation Split: 80% training / 20% validation
• Selected Features:
  • Pclass
  • Sex
  • Age
  • SibSp
  • Parch
  • Fare
  • One-hot encoded Embarked

The model was trained and evaluated using basic performance metrics.

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📈 Results

• Achieved a public Kaggle score of 0.76076
• This was the first version of the model and performed well on the leaderboard.

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🚀 Next Steps

Planned improvements and experiments:

• Try other models (e.g., Logistic Regression, XGBoost)
• Perform hyperparameter tuning using GridSearchCV
• Use feature importance to select or engineer better features
• Consider using cross-validation for more reliable evaluation

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🔗 Resources

• 📓 Kaggle Notebook: Titanic - Random Forest v1
• 🏆 Competition Page: Kaggle Titanic

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🧑‍💻 Author

Kaggle: kaggle.com/busradeveci