Values Skills: Data pre-processing, descriptive statistics, Python
Skills: Regression methods, Prediction methods. web link
You work for the city of Seattle. To achieve its goal of a carbon-neutral city in 2050, your team is taking a close interest in emissions from non-residential buildings. For this, careful records were made by your agents in 2015 and 2016.However, these surveys are expensive to obtain, and from those already done, you want to try to predict the emissions of buildings whoseemissionshave not yet been measured.Two measures interest you: CO2 emissions and total energy consumption. You also want to evaluate the interest in the emission prediction of the ENERGYSTAR Score(which is complicated to calculate)with the approach currently used by your team.
Google colab -To perform Data analysis.
pip intsall pandas
pip intsall matplotlib
pip3 install folium
pip install seaborn
pip install numpyimport pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import folium
import numpy as np
from folium import plugins
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Following plot gives GHE emission with respect to location.
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Following plot gives GHE emission heat map with respect to Seatle.
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Following plot gives GHE emission with respect to location.
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Following plot gives GHE emission heat map with respect to Seatle.
colour value for 'GHGEmissions(MetricTonsCO2e)' in world map
- Green - GHE emission< 10
- lightgreen - 10<'GHE emission'>20
- pink - 20<'GHE emission'>30
- black - 30<'GHE emission'>40
- purple - 40<'GHE emission'>50
- grey - 50<'GHE emission'>100
- brown - 100<'GHE emission'>150
- yellow - 150<'GHE emission'>200
- lightblue - 200<'GHE emission'>250
- dark blue- 250<'GHE emission'>500
- orange - 500<'GHE emission'>1000
- red - GHE emission>1000
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