/ClassicTitanic

This is a project that Me and @mohbarati are doing as a hobby/learning get familiar with the ML world. (2nd)

Primary LanguageJupyter Notebook

Kaggle Titanic Competition

This repository contains my solution for the Kaggle Titanic Machine Learning competition.

Overview

The Titanic competition is a classic machine learning problem where the goal is to predict whether a passenger survived or not based on various features such as age, sex, ticket class, etc.

Dataset

The dataset for this competition contains information about passengers aboard the Titanic, including whether they survived or not. It's divided into two sets: training set (with labels) and test set (without labels). The data is provided in CSV format.

File Descriptions

  • train.csv: Training dataset containing features and labels.
  • test.csv: Test dataset containing only features.

Dependencies

  • Python 3
  • Pandas
  • NumPy
  • Scikit-learn

Approach

I approached this problem using a combination of exploratory data analysis, feature engineering, and machine learning algorithms such as Random Forest and Gradient Boosting.

Usage

  1. Clone this repository.
  2. Install the dependencies using pip install -r requirements.txt.
  3. Download the dataset from Kaggle and place train.csv and test.csv in the data/ directory.
  4. Run the Jupyter notebook titanic.ipynb to see the data analysis and model building process.
  5. Use the trained model to make predictions on the test set.

Results

My final submission achieved an accuracy of X% on the Kaggle leaderboard.

Credits

This project was completed by Sadeq Soltani.

License

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