Duration:
70 hours
 
Project Overview:
Predict energy consumption using machine learning techniques. This project involves data cleaning, feature engineering, model training, and evaluation to develop accurate predictive models that can aid in climate action and energy optimization.
 
Key Learning Outcomes:
Data Cleaning & Preprocessing: Prepare datasets by handling missing values and filtering relevant features.
Feature Engineering: Create new features to enhance model performance.
Exploratory Data Analysis (EDA): Analyze data distributions and relationships to inform modeling strategies.
Machine Learning Model Training: Train and benchmark various models like Ridge Regression, RandomForest, and XGBoost.
Model Evaluation & Optimization: Assess model accuracy using metrics such as R², RMSE, and MAE, and optimize hyperparameters.
Interpretability: Use tools like SHAP to understand feature importance and model behavior.
 
Tools & Libraries:
Programming Language: Python
Libraries: pandas, seaborn, matplotlib, scikit-learn, xgboost, shap
 
Deliverables:
Cleaned Dataset: A refined dataset ready for modeling.
Data Cleaning Notebook: Documentation of data preprocessing steps.
Feature Engineering Notebook: Details of feature creation and optimization.
Model Training Notebook: Code for training and benchmarking machine learning models.
Evaluation Report: Comprehensive evaluation of model performance with visualizations.
Final Model and Notebooks: The best-performing model along with analysis and visualizations.
 
Who Should Enroll:
Individuals interested in applying machine learning to energy and sustainability.
Aspiring data scientists looking to work on real-world predictive modeling projects.
Professionals aiming to leverage data for climate action and energy optimization.
 
Why This Project? This project provides practical experience in predictive modeling within the energy sector, emphasizing the importance of data-driven decisions in sustainability. You'll develop skills that are highly relevant in today's efforts to combat climate change through optimized energy usage.