Predicting Building Consumption Needs

This project focuses on predicting the energy consumption needs of buildings using supervised machine learning techniques. I've enhanced my data analysis skills by learning advanced feature engineering techniques and applying them to create relevant variables for machine learning models. The project also involved training and evaluating various supervised learning algorithms to predict future consumption patterns. Additionally, I've explored Explainable AI (XAI), which allowed me to interpret and explain how the features influence model predictions, an increasingly important skill in modern data science.

Key Skills Acquired:

Feature Engineering: Developing and selecting relevant features that improve model accuracy and performance. You’ll learn how to transform raw data into meaningful inputs for machine learning models.
Supervised Learning: Using machine learning algorithms (e.g., Linear Regression, Random Forests, Gradient Boosting) to predict building consumption needs based on historical data.
Model Selection and Evaluation: Understanding the strengths and weaknesses of various supervised learning models and evaluating their performance using metrics like MAE (Mean Absolute Error), RMSE (Root Mean Squared Error), and R².
Explainable AI (XAI): Gaining an understanding of how machine learning models make predictions and using tools like SHAP or LIME to explain the impact of different features on model outputs.
Data Preprocessing: Cleaning and transforming data to handle missing values, outliers, and ensure that it is ready for machine learning tasks.
Model Optimization: Tuning hyperparameters to improve model performance and avoid overfitting or underfitting.

Technologies Used:

Python: The primary programming language for data manipulation, feature engineering, and machine learning modeling.
scikit-learn: A powerful library for implementing machine learning algorithms, performing model evaluation, and tuning hyperparameters.
Pandas & NumPy: Data manipulation and preprocessing.
SHAP / LIME: Libraries for explaining machine learning model predictions and interpreting feature importance in an understandable way.
Matplotlib / Seaborn: Visualization libraries for analyzing and presenting data and model results.