Kaggle-Titanic-Classifier

This project focuses on leveraging neural network models to predict the survival of passengers on the Titanic, as part of the Kaggle Titanic competition.

Key Features

• Utilizes a neural network architecture for survival prediction.
• Implements feature engineering techniques for enhanced model input.
• Includes separate CSV files for gender-based and individual submissions.
• Combines and submits predictions for comprehensive evaluation.

Files

• train.csv: Training dataset with labeled survival information.
• test.csv: Test dataset for which predictions are to be made.
• gender_submission.csv: Baseline submission file based on gender.
• my_submission.csv: Custom submission file generated using neural network model.
• combined_submission.csv: Merged submission file for evaluation.

Instructions

1. Explore the Jupyter Notebook (titanic_kaggel.ipynb) for detailed code and analysis.
2. Experiment with neural network architecture, layers, and activation functions.
3. Use the provided CSV files for Kaggle competition submissions.
4. Feel free to contribute or fork for further enhancements.

Notes

• Perfect accuracy is challenging due to inherent noise in the dataset. Aim for realistic improvements using neural network techniques.
• Explore advanced strategies for better results.

How to Run

1. Install the required dependencies by running:

   pip install -r requirements.txt