🏢 Predicting Seattle buildings consumption and emissions with machine learning

OPENCLASSROOMS - Data Scientist - Project 4

This repository contains notebooks for a machine learning project that predicts energy consumption and greenhouse gas emissions based on various features.

📊 Data

The dataset used for this project is the Seattle 2016 Building Energy Benchmarking, which includes information on various features of buildings in Seattle city.

📁 Files

• barbier_victor_1_notebook_exploratoire_092022.ipynb : Exploratory data analysis of the buildings features
• barbier_victor_2_notebook_prediction_energy.ipynb : Machine learning models for the prediction of energy consumption
• barbier_victor_2_notebook_prediction_ghge.ipynb : Machine learning models for the prediction of greenhouse gas emissions
• barbier_victor_4_presentation_092022.pdf: Final presentation of the project

🛠️ Tools

• Python 3.x
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib / Seaborn
• Scikit-learn
• XGBoost
• Lime