AhmedBassoul1/Morocco-House-Price-Prediction

This repository serves as a hands-on platform for exploring and implementing Machine Learning Operations (MLOps) concepts. It aims to provide a comprehensive environment for learning, experimentation, and collaboration. # 🏠 Morocco House Price Prediction An end-to-end MLOps project that predicts housing prices in Morocco using a **ZenML + MLflow** training and deployment pipeline. The project ingests a Moroccan real-estate dataset, runs full EDA, handles missing values and outliers, applies feature engineering, trains a Linear Regression model inside a scikit-learn pipeline, tracks experiments with MLflow, and finally deploys the model as a local prediction service. --- ## 📁 Project Structure ``` . ├── analysis/ # Exploratory Data Analysis │ ├── EDA.ipynb # Main EDA notebook │ └── analyze_src/ # Reusable analysis strategy classes │ ├── basic_data_inspection.py │ ├── missing_values_analysis.py │ ├── univariate_analysis.py │ ├── bivariate_analysis.py │ └── multivariate_analysis.py │ ├── data/ # Raw data │ └── housing_data.zip ├── extracted_data/ # Auto-extracted from the zip │ └── housing_data.csv ├── cleaned_data/ # Cleaned dataset used by the pipeline │ └── cleaned_housing_data.csv ├── figures/ # EDA plots (distributions, heatmaps, etc.) │ ├── src/ # Core ML logic (Strategy pattern based) │ ├── ingest_data.py │ ├── handle_missing_values.py │ ├── feature_engineering.py │ ├── outlier_detection.py │ ├── data_splitter.py │ ├── model_building.py │ └── model_evaluator.py │ ├── steps/ # ZenML pipeline steps (wrappers around src/) │ ├── data_ingestion_step.py │ ├── handle_missing_values_step.py │ ├── feature_engineering_step.py │ ├── outlier_detection_step.py │ ├── data_splitter_step.py │ ├── model_building_step.py │ ├── model_evaluator_step.py │ ├── dynamic_importer.py │ ├── model_loader.py │ ├── prediction_service_loader.py │ └── predictor.py │ ├── pipelines/ # ZenML pipeline definitions │ ├── training_pipeline.py │ └── deployment_pipeline.py │ ├ …