lisa-liy1/titanic_ml_my_first_competition_kaggle577

This repository contains my solution for the Titanic Machine Learning competition on Kaggle. It's my first competition, and I explore various machine learning techniques to predict passenger survival. The Jupyter notebook includes data analysis, feature engineering, and model training. Feel free to explore, learn, and contribute!

🚢 Titanic Machine Learning Competition

🚀 Overview

Welcome to my Titanic Machine Learning competition project! This repository contains my solution for the Titanic Kaggle competition. Whether you're a beginner or experienced, this project is designed to help you understand machine learning and data science techniques.

ℹ️ Kaggle Competition Details

• Competition Link: Titanic: Machine Learning from Disaster
• Objective: Predict the survival of passengers on the Titanic based on provided data.

📁 Repository Contents

• notebooks/: Jupyter notebooks with step-by-step analysis, feature engineering, and model training.
• data/: Dataset files.
• images/: Visualizations and images used in the notebooks.

💻 Getting Started for Beginners

1. Clone the repository:

   git clone https://github.com/Farhakousar1601/titanic_ml_my_first_competition_kaggle.git

2. Navigate to the project directory:

   cd titanic_ml_my_first_competition_kaggle

3. Follow the Kaggle Competition Link: Titanic: Machine Learning from Disaster

4. Join the Competition:

   • Click on "Join Competition" to participate.
   • Download the dataset from the "Data" tab on the competition page.

5. Start the Jupyter Notebooks:

   • Open the Jupyter notebooks in the notebooks/ directory.
   • Follow the instructions and code provided in the notebooks to analyze the Titanic dataset and develop machine learning models.

✨ Project Highlights

• Utilized advanced machine learning algorithms for accurate predictions.
• Explored insightful visualizations and feature engineering techniques.

👀 Preview

🛠️ Technologies Used

• Python
• Jupyter Notebooks
• Scikit-learn
• Pandas
• Matplotlib
• Seaborn

🏆 Achievements

• Top 10% in Kaggle competition.

📜 License

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

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👉 Access the Kaggle Kernel here.

Feel free to connect with me on Kaggle, LinkedIn, and GitHub for more updates!