This project provides a framework for building energy prediction models. It includes components for data ingestion, transformation, validation, evaluation, and model training, along with utilities for logging, handling exceptions, and managing configurations. This project offers a comprehensive framework for developing machine learning models to predict building energy consumption. It empowers you to build, train, and evaluate models that can help optimize energy usage in buildings.
Modular Design: The project is organized into well-defined components for data ingestion, transformation, validation, evaluation, and model training. This promotes maintainability, reusability, and easier testing. Data Handling: Ingestion: Load data from various sources, allowing flexibility in data collection methods. Transformation: Clean, preprocess, and engineer features from your data to enhance model performance. Validation: Ensure data quality by checking for missing values, outliers, and adherence to expected schema. Model Training: Train various machine learning models suitable for energy prediction tasks. Evaluation: Assess model performance using relevant metrics. Prediction Pipeline: Utilize the trained model to make predictions on new data, enabling you to estimate future energy consumption. Utilities: Leverage helper functions for common tasks like logging and exception handling. Configuration Management: Potentially includes functionality for managing project-wide settings through constants or a configuration entity. Benefits:
Improved Energy Efficiency: By accurately predicting energy usage, you can identify areas for optimization, leading to cost savings and a reduced environmental footprint. Data-Driven Decision Making: The project allows you to analyze past energy consumption patterns and make informed decisions about building operations and upgrades. Flexibility: Supports various data sources and model architectures, providing a customizable solution for different building types and needs.
init.py (empty): Marks this directory as a Python package.
data_ingestion.py: Contains functions for loading data from various sources.
data_transformation.py: Implements data cleaning, preprocessing, and feature engineering.
data_validation.py: Ensures data quality by checking for missing values, outliers, and schema adherence.
data_evaluation.py: Defines metrics to evaluate model performance on unseen data.
model_trainer.py: Handles training and saving machine learning models for energy prediction.
init.py (empty): Marks this directory as a Python package.
common.py: Provides general utility functions used throughout the project.
init.py (empty): Marks this directory as a Python package.
Potentially contains logging configuration and setup.
init.py (empty): Marks this directory as a Python package.
Could include custom exception classes for handling errors.
init.py (empty): Marks this directory as a Python package.
training_pipeline.py: Orchestrates the entire training process, including data preparation and model training.
prediction_pipeline.py: Facilitates making predictions on new data using the trained model.
init.py (empty): Marks this directory as a Python package.
Likely stores project-wide constants like file paths, model parameters, etc.
init.py (empty): Marks this directory as a Python package.
Could hold data structures or classes representing entities used in the project (e.g., ConfigEntity).
config_entity.py: Might define a class to encapsulate configuration settings.
end_point.py: Potentially defines a web API endpoint for making predictions.