This repository contains a Random Forest model implementation for predicting survival outcomes of passengers on the Titanic shipwreck. The model is built to participate in the legendary Titanic ML competition on Kaggle, which serves as an excellent entry point for individuals interested in machine learning competitions.
The goal of the competition is to develop a machine learning model that accurately predicts whether a passenger survived the Titanic disaster or not. By leveraging the available dataset, we aim to uncover patterns and relationships that can assist in making informed predictions about survival outcomes.
The dataset provided for this competition contains information about various passengers on the Titanic, including their demographic attributes, ticket information, and survival status. The dataset is divided into two subsets: a training set and a test set. The training set includes survival labels for each passenger, while the test set lacks this information, requiring our model to make predictions solely based on the provided features.
In this repository, we implement a Random Forest model to tackle the Titanic survival prediction problem. Random Forest is an ensemble learning algorithm that combines multiple decision trees to make accurate predictions. By averaging the predictions of individual decision trees, Random Forest can handle complex relationships between features and provide robust predictions.
The repository is structured as follows:
'train.csv/' : This directory contains the training and test datasets in CSV format. Ensure that the datasets are placed here before running the model. test.csv/: This directory contains the training and test datasets in CSV format. Ensure that the datasets are placed here before running the model. Titanic.knwf/: This directory contains the source code for the Random Forest model implementation. README.md: This file provides an overview of the repository and instructions for running the model. Getting Started To use this repository and train the Random Forest model on the Titanic dataset, follow these steps:
Clone this repository to your local machine using git clone. Ensure you have Python 3.6 or higher installed. Install the required dependencies by running pip install -r requirements.txt. Place the training and test datasets in the data/ directory. Run the data preprocessing script: python src/data_preprocessing.py. This script handles missing values, performs feature engineering, and normalizes the data. Train the Random Forest model by running: python src/model_training.py. This script trains the model on the preprocessed data. Once the model is trained, make predictions on new data using: python src/prediction.py. Feel free to explore and modify the code to improve the model's performance and experiment with different techniques.
If you find any issues or have suggestions for improvement, please feel free to open an issue or submit a pull request. Contributions are always welcome!
This project is licensed under the MIT License. Feel free to use the code provided here for your own projects