Water4Cities project - Webinar #1.
- data gathering infrastructure (adopted from Optimum project) for collecting Ljubljana aquifer groundwater data, weather data from darksky.net and Skiathos pumping data from legacy system Excel files
- API for data retrieval (also adopted from Optimum project) on the previously mention datasets
- data cleaning infrastructure (on Ljubljana aquifer data and present the results)
- data fusion infrastructure on a stream of smart-grid data
- simple and fast data-driven modelling capabilities on the top of W4C data gathering infrastructure with the usage of Python/scikit-learn/pandas.
- Anaconda (http://conda.io/docs/user-guide/install/index.html)
- Git (http://git-scm.com/downloads) (optional)
The following code with python imports should work without errors in your Jupyter notebook.
import urllib.request
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import cross_val_score
from sklearn import linear_model
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score