Price Prophet: An end-to-end MLOps Pipeline for House Price Prediction

Price Prophet is a production-grade machine learning pipeline built to predict house sale prices using a robust, scalable and reproducible MLOps framework.

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FILES & STRUCTURE

• data/: Raw zipped data from Kaggle
• extracted_data/: Ingested dataset
• analysis/: Exploratory notebooks and analyzers
• src/: Core modules for feature engineering, model building, evaluation
• steps/: ZenML-defined step-wise modular pipeline stages
• pipelines/: Training and deployment pipeline definitions
• pipeline_runs: Pipeline Runs in form of DAG visualizations, from ZenML Dashboard
• run_pipeline.py: Executes training pipeline
• run_deployment.py: Executes deployment/inference pipeline
• app.py: Streamlit interface for user-side predictions
• sample_predict.py: Local REST inference - single sample
• sample_batch_predict.py: Local REST inference - batch prediction
• exported_model/: Artifacts of one of the best models manually saved via MLflow
• requirements.txt: Python dependencies

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IMPORTANT LINKS

• Dataset Utilized (Kaggle): Ames Housing Dataset
• Original Dataset (Kaggle): House Prices- Advanced Regression Techniques
• Python Notebook (Kaggle): House Pricing EDA and Extensive Modeling

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INTRODUCTION

• Accurate house price prediction is vital for real estate valuation, investment, and decision-making.
• Traditional ML workflows often suffer from:
  • Poor reproducibility and pipeline modularity
  • Lack of production-readiness and deployment integration
  • Minimal tracking or model lifecycle management
• Price Prophet addresses this by building a clean, reproducible, and production-ready ML pipeline from ingestion to deployment.
• Built with Python, ZenML, MLflow, and Streamlit, it ensures seamless orchestration, experiment tracking, deployment, and user-friendly inference.

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PROBLEM DEFINITION

• Manual Workflows: Traditional house price prediction lacks automation, requiring repetitive preprocessing, model training, and evaluation steps.
• Pipeline Gaps: Most ML solutions stop at model accuracy, missing crucial components like deployment, tracking, and maintainability.
• Lack of Production Readiness: Existing approaches don't support reproducible, scalable, or monitorable model deployment in real-world settings.
• End-to-End MLOps: There is a clear need for a robust, automated pipeline integrating data handling, modeling, versioning, and serving with real-time inference.

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OBJECTIVES

• Ultimate Aim: Build an end-to-end MLOps pipeline.
• Perform robust data processing and heavy feature engineering so as to get best model performance.
• Utilize and compare multiple regression strategies for price prediction.
• Integrate MLOps tools like ZenML and Mlflow.
• Build a front-end application for user interaction and visualization.
• Eensure production readiness by focusing on modularity, reproducibility, version control, and real-time prediction capability.

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METHODOLOGY

Pipeline Workflow

Core ML Stages

Stage	Description
Data Ingestion	Loads and extracts raw housing data from compressed archives (archive.zip).
Initial Preprocessing	Cleans missing values and duplicates; prepares dataset for transformation.
Feature Engineering	Applies log-transformations and constructs domain-inspired features like Porch, Bath_total, and FinSF.
Outlier Handling	Identifies and removes extreme values from critical features such as SalePrice.
Data Splitting	Splits data into train/test using stratified sampling while preserving target distribution.
Model Building	Trains a stacked ensemble using base models (XGBoost, LightGBM) with meta-model (Linear Regression).
Model Evaluation	Computes RMSE, MSE, and R² metrics using MLflow logging and visualization.
Deployment Preparation	Logs the model artifacts and expected columns to MLflow for reproducible serving.

MLOps Stack

Model Deployment

• Deployment via MLflow Model Deployer Service (Not suitable for Windows OS)
  

• Manual MLflow Model Serving via REST API (Works for MAC/Windows OS)
  

Inference

• Batch Inference (Local REST API):

  • Once the model is served manually using MLflow, predictions can be made by sending input data (as JSON) via HTTP POST to the /invocations endpoint.
  • A sample_batch_predict.py script is used to load a .csv file, send data to the model server, and save predictions in predictions.csv.

• Real-Time Inference (Streamlit Application):

  • A user-friendly UI built with Streamlit allows manual input or CSV uploads.
  • Sends the data to the same REST endpoint and displays predicted house prices instantly.
  • Supports downloading predictions and visualization inside the web app.

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RESULTS

• High Level EDA

• Comparative Model Evaluation Metrics
  

• Streamlit Application (Interface)

• Making Predictions on App using second mode

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INSTALLATION

To set up the project on your local machine, follow these steps:

1. Clone the repository:

    https://github.com/krishnaura45/price-prophet.git
    cd price-prophet

2. Install dependencies:

    pip install -r requirements.txt

3. Run training pipeline:

    python run_pipeline.py

4. Serve model manually (use MLflow UI to fetch run ID):

    mlflow models serve -m "runs:/<your_run_id>/model" -p 1234 --no-conda

5. Run deployment pipeline:

    python run_deployment.py

6. Run the Streamlit app:

    streamlit run app.py

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CONTRIBUTING

• Fork the repository.
• Create a new branch.
• Commit changes with clear messages.
• Submit a pull request.
• Ensure new features are tested and documented.

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TECH STACK

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FEATURES

• Modular ZenML Steps (each in steps/)
• Advanced EDA and feature insights (analysis/)
• Model evaluation with proper metrics
• Manual model deployment (you control what gets served)
• Streamlit App for UI-based input, visualization and download

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FUTURE SCOPE

• Cloud-Native Deployment: Containerize the pipeline using Docker and orchestrate via Kubernetes to enable scalable, consistent, and production-ready deployments across cloud platforms.
• Drift Detection & AutoML: Implement data drift monitoring (e.g., with Evidently/WhyLabs) and integrate AutoML frameworks for continual model retraining and optimization.
• Model Explainability: Enhance interpretability using SHAP or LIME and display visual explanations in Streamlit for better decision trust and transparency.

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REFERENCES

1. ZenML Docs - https://docs.zenml.io/
2. MLflow Docs - https://mlflow.org/docs/latest/index.html
3. CatBoost Documentation - https://catboost.ai/en/docs/

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Contributors 🧑‍💼

• Krishna Dubey (Pipeline design, ML modeling, deployment, UI dev)