INFO 523 - Fall 2023 - Project Final
Dallas vs. Arlington vs. Denton
Conduct EDA to identify patterns and relationships in the climatic data.
This can include visualizations like time series plots, histograms, and scatter plots to understand temperature trends, humidity levels, etc.
Create and separate the data by “Seasons” and then create thresholds for each season based on the following weather conditions:
Hourly Dry Bulb Temperature
Hourly Relative Humidity
Hourly Wind Speed
Design a classification function to label UHI intensities for hourly weather data.
def classify_uhi(row, temp_thresholds, humidity_thresholds, wind_speed_thresholds):
season = row['Season']
temp = row['HourlyDryBulbTemperature']
humidity = row['HourlyRelativeHumidity']
wind_speed = row['HourlyWindSpeed']
# Get the thresholds for the current season
temp_high = temp_thresholds.loc[season, 0.50]
temp_medium = temp_thresholds.loc[season, 0.25]
humidity_low = humidity_thresholds.loc[season, 0.25]
wind_speed_low = wind_speed_thresholds.loc[season, 0.25]
# Classify based on combined criteria
if temp > temp_high and humidity < humidity_low and wind_speed < wind_speed_low:
return 'High'
elif temp > temp_medium:
return 'Medium'
else:
return 'Low'
Developed 5 classification models:
Decision Tree
Random Forest
XGBoost
Gradient Boost
SVM Classifier