Titanic Survival Prediction using Random Forest

Overview

This project applies machine learning to predict passenger survival on the Titanic. The dataset is processed, and a Random Forest classifier is trained to predict whether a passenger survived based on available features.

Dataset

The dataset used is the Titanic dataset from Kaggle, which contains passenger details such as age, gender, fare, and class.

Steps

1. Read the CSV file: The dataset is loaded using pandas.read_csv().
2. Preprocess the data:
   • The target variable (Survived) is extracted.
   • Unnecessary columns (Name, Ticket, Cabin) are dropped.
   • Categorical features are converted into numerical representations using one-hot encoding (pd.get_dummies()).
   • Missing values in the Age column are filled with the mean age.
3. Split the dataset:
   • The dataset is split into training (70%) and testing (30%) sets using train_test_split().
4. Train the model:
   • A RandomForestClassifier with 100 trees and a max depth of 5 is trained on the training data.
5. Make Predictions:
   • The trained model predicts survival on the test set.
6. Evaluate Accuracy:
   • The model's accuracy is calculated using np.mean(predictions == y_test), achieving ~85% accuracy.
7. Save Results:
   • Predictions can be saved to a CSV file for submission.

Dependencies

Ensure you have the following Python libraries installed:

pip install pandas numpy scikit-learn

Future Improvements

• Tune hyperparameters for better performance.
• Use feature engineering to extract more insights from existing data.
• Try different models like Logistic Regression, XGBoost, or Neural Networks.