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aakashsharma003/price-predictor-system
This project is a complete ML pipeline, from data preprocessing to model deployment using ZenML & MLflow. I Implemented with best practices!
Price Predictor System - Setup Guide
Prerequisites
Make sure you have the following installed before proceeding:
- Python (>=3.8)
- Pip (latest version)
- Virtual Environment (
venv) - ZenML
- MLflow
Step 1: Create a New Folder
Create a new folder to contain the project and navigate into it:
mkdir price_predictor_system
cd price_predictor_systemStep 2: Set Up a Virtual Environment
On Mac:
python3 -m venv vnvOn Windows:
python -m venv vnvStep 3: Activate the Virtual Environment
On Mac:
source vnv/bin/activateOn Windows:
vnv\Scripts\activateOnce activated, you should see (vnv) at the beginning of your terminal prompt.
Step 4: Install Required Packages
Make sure you have a requirements.txt file with the necessary dependencies. Install them using:
pip install -r requirements.txtExample requirements.txt File:
zenml
mlflow
pandas
numpy
scikit-learn
matplotlib
Step 5: Install ZenML Integrations
ZenML requires specific integrations for MLflow. Install them using:
zenml integration install mlflow -yStep 6: Configure ZenML Stack
ZenML requires a stack configuration for MLflow experiment tracking and model deployment. Run the following commands:
zenml experiment-tracker register mlflow_tracker --flavor=mlflow
zenml model-deployer register mlflow --flavor=mlflow
zenml stack register local-mlflow-stack -a default -o default -d mlflow -e mlflow_tracker --setStep 7: Running the Project
To run the project, execute the following command:
python run_deployment.pyMake sure the virtual environment is activated before running any command.
Notes:
- Always activate the virtual environment before running the project.
- Ensure that all dependencies are installed correctly.
- If you encounter any errors, check the installed packages using:
pip list
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Created January 31, 2025
Updated January 31, 2025