End-to-End Machine Learning Process

This repository demonstrates an end-to-end machine learning process. The project includes the following steps:

1. Data Collection: Gathering and preparing data for analysis.
2. Data Preprocessing: Cleaning and transforming raw data to be suitable for the machine learning model.
3. Feature Engineering: Creating new input features from the existing ones.
4. Model Training: Building and training the machine learning model.
5. Model Evaluation: Assessing the performance of the trained model.
6. Model Optimization: Tuning the model to achieve better performance.
7. Model Deployment: Deploying the trained model to a production environment.
8. Monitoring: Keeping track of the model's performance over time.

The repository is organized as follows:

• data/: Contains the raw and processed data files.
• src/: Contains the source code for the project.
• models/: Contains the trained machine learning models.
• notebooks/: Contains Jupyter notebooks for exploratory data analysis and model training.
• docs/: Contains documentation for the project.

We use DVC for data versioning and experiment tracking, and DagsHub for collaboration and project management. Feel free to explore the repository and reach out if you have any questions or suggestions.

pip3 install dagshub --upgrade
dagshub login
mkdir data
echo '## Data will be uploaded to this folder' >> data/readme.md
dagshub upload saifulrijal873/e2e-machine-learning data/ data/
git pull