This repository contains the code and resources for an end-to-end machine learning project. The goal of this project is [state your project goal here].

The project is structured as follows:

• data/: Contains the dataset used for training and testing.
• models/: Contains trained machine learning models.
• notebooks/: Jupyter notebooks used for data exploration, preprocessing, model training, and evaluation.
• src/: Source code for the machine learning pipeline.
• requirements.txt: Lists all dependencies required to run the project.

Follow these instructions to get the project up and running on your local machine.

1. Clone the repository:

   git clone https://github.com/your_username/your_project.git

2. Install dependencies:

   pip install -r requirements.txt

3. Explore the notebooks:

   Navigate to the notebooks/ directory and open the Jupyter notebooks to understand the data and the machine learning pipeline.

4. Train models:

   Use the provided notebooks or run the scripts in the src/ directory to train machine learning models.

5. Evaluate models:

   Evaluate the trained models using the evaluation metrics provided in the notebooks or scripts.

6. Deploy models (optional):

   If needed, deploy the trained models to production using the provided deployment scripts.

• Python (>=3.6)
• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn

• #machine-learning
• #python
• #scikit-learn
• #data-science