Seattle Energy: automatising an administrative task
About this Project
In this project, we are working for the city of Seattle and with its open data. The goal is to facilitate the building energy statement generation for the non-residential buildings.
Complex and complete energy statements have been made by the city, but they represent a big effort and our task is to simplify the process using our Data Science skills.
In this project, we have multiple objectives:
- Predict the energy consumption and green house gas emissions of the buildings,
- Investigate the EnergyStar score and determinate if it is relevant for the greenhouse gas emissions predictions.
In order to fullfill these objectives:
- We are going to implement custom Sklearn transformers to create a pipeline for our data.
- Then, we train and evaluate multiple machine learning models to select the best estimator for our problematic.
Features
Artificial Intelligence
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Data Analysis
Keep improving your results! With Data-Analysis we transform the raw data from your app into meaningful insights (and beautiful graphs) which will allow to understand your users and market.
Technologies
Data/Ai
NumPy
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
source: wikipedia.org
Pandas
Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
source: pandas.pydata.org
Plotly
Plotly's Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.
source: plotly.com
Sickit-Learn
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language.
It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project.
source: wikipedia.org
XGBoost
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
source: xgboost.readthedocs.io
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Q&A
If you are still wondering...
Hikonic is a web development agency founded by Thibgl, a passionate data scientist and full-stack engineer based in Bordeaux, France. With a commitment to delivering cutting-edge solutions, Hikonic specializes in blending innovative technologies with elegant designs to create powerful digital products. Whether you're a small business owner or a large enterprise, Hikonic tailors its services to meet your unique needs and goals.
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