1. Create Conda Environment

   conda create -p venv python==3.10 -y

   This command creates a new conda environment with Python 3.10 in the venv directory.

2. Add venv to .gitignore

   To prevent the venv folder from being tracked by git, add the following line to your .gitignore file:

   venv/
   

   This ensures the environment files are not included in version control.

   3. Install Requirements

      To install the required Python packages, use the requirements.txt file with the following command:

      pip install -r requirements.txt

      This will install all dependencies listed in requirements.txt into your conda environment.

1. Data Ingestion: Collect and preprocess data for analysis.
2. Data Transformation and validation: Cleanse and transform raw data into a usable format.
3. Model Training: Train machine learning models on transformed data.
4. Model Evaluation: Assess model performance using appropriate metrics. -- ML Flow and dagshub
5. Deployment: Deploy trained models for real-world applications or predictions.

1.update config.yaml 2.Update schema.yaml 3.Update params.yaml 4.Update entity 5.Update configuration manager 6.Update components 7.Update pipeline.py 8.update main.py