Welcome to the Kaggle Challenges ML Repository! This repository hosts machine learning code and solutions for various Kaggle challenges. Whether you're a beginner looking to learn machine learning or an experienced practitioner aiming to improve your skills, this repository aims to provide valuable resources and solutions to help you tackle Kaggle challenges effectively.
To get started with this repository, follow these steps:
-
Clone the Repository: Clone this repository to your local machine using the following command:
git clone https://github.com/your-username/kaggle-challenges-ml.git -
Install Dependencies: Install the required dependencies by running:
pip install -r requirements.txt -
Explore Challenges: Explore the available challenges in the repository. Each challenge is organized into a separate directory, containing the dataset, code, and solution.
-
Run Code: Open the Jupyter notebooks or Python scripts to explore the code and solutions for each challenge. Execute the code to see the results and understand the approach taken to solve the problem.
The repository is organized into the following directory structure:
kaggle-challenges-ml/
│
├── challenge1/
│ ├── dataset/
│ │ └── ... (dataset files)
│ ├── code/
│ │ └── ... (Jupyter notebooks, Python scripts)
│ └── README.md
│
├── challenge2/
│ ├── dataset/
│ │ └── ... (dataset files)
│ ├── code/
│ │ └── ... (Jupyter notebooks, Python scripts)
│ └── README.md
│
└── ...
Each challenge directory contains the following subdirectories:
dataset: Contains the dataset files required for the challenge.code: Contains the code, including Jupyter notebooks or Python scripts, used to solve the challenge.README.md: Provides information about the challenge, including problem statement, dataset details, approach, and results.
This repository is licensed under the MIT License. See the LICENSE file for details.
Happy coding and happy Kaggle-ing! 🚀🔍✨