/udacity-mlnd-ev-capstone

Capstone Project for the Udacity Machine Learning Nanodegree

Primary LanguageJupyter Notebook

udacity-mlnd-ev-capstone

Capstone Project for the Udacity Machine Learning Nanodegree

Salvador Joel Núñez Gastélum

Project Overview

This project applies data augmentation techniques and a combination of k-means clustering, K-Nearest Neigbors (KNN), and Gradient Boosted Decision Trees (GBDT) to predict whether there is an Electric Vehicle (EV) charging at a house during a specific time frame.

For more details, see original data set here.

Project Instructions

  1. Clone the repository and navigate to the downloaded folder.
git clone https://github.com/torenunez/udacity-mlnd-ev-capstone.git
cd udacity-mlnd-ev-capstone

  1. Create (and activate) a new environment with Python 3.6.

    • Linux or Mac:
    conda create --name ev-capstone python=3.6
source activate ev-capstone
    • Windows:
    conda create --name ev-capstone python=3.6
activate ev-capstone

  2. Install a few pip packages.

pip install -r requirements.txt
  1. Create an IPython kernel for the ev-capstone environment. Open the notebook.
python -m ipykernel install --user --name ev-capstone --display-name "EV Capstone Project"
jupyter notebook ev-capstone.ipynb
  1. Before running code, change the kernel to match the ev-capstone environment by using the drop-down menu (Kernel > Change kernel > ev-capstone).