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Modelling/Regression model.py
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import numpy as np | |
import pandas as pd | |
from sklearn.linear_model import LinearRegression | |
from sklearn.model_selection import train_test_split | |
import matplotlib.pyplot as plt | |
from sklearn import linear_model | |
import statsmodels.api as sm | |
import statsmodels.formula.api as smf | |
df = pd.read_csv(r'C:\Users\ojt98\OneDrive\Desktop\Python\weather_data_24hr.csv') | |
#df=pd.get_dummies(df, prefix='weather', columns=['weather']) | |
X=df[["windspeedKPH", "humidity", "avgtempC"]] | |
y=df[["accidents"]] | |
lm = linear_model.LinearRegression() | |
model = lm.fit(X,y) | |
predictions = lm.predict(X) | |
#print(predictions[0:5]) | |
print(lm.score(X,y)) ##R^2 | |
arr=lm.coef_ | |
print(arr) | |
for word in arr: | |
one=(word[0]) | |
for word in arr: | |
two=(word[1]) | |
for word in arr: | |
three=(word[2]) | |
##pull live data into this below... | |
#accidents=one(windspeedKPH)+ two(humidity)+ three(avgtempC) | |
# python_sub.py | |
#mosquitto_pub -h mqtt.dioty.co -p 8883 -u <your user-id> -P <your password> -t <topic> -m <message> -r --cafile ./dioty_ca.crt | |
import paho.mqtt.client as mqtt | |
import json, os | |
from datetime import date, datetime | |
import time | |
def on_connect(client, userdata, flags, rc): | |
if rc==0: | |
client.connected_flag = True | |
print("connected OK") | |
client.subscribe("iot/twiningo") | |
else: | |
print("Bad connection Returned code=",rc) | |
def on_message( client, userdata, msg ): | |
print('in on_message') | |
""" callback function for messages received """ | |
topic = msg.topic | |
data = msg.payload.decode("utf8") | |
print(topic) | |
print(data) | |
client = mqtt.Client() | |
client.on_connect = on_connect | |
client.on_message = on_message | |
client.username_pw_set("iot/twiningo", "JLjsTIe2Ws") | |
client.tls_set("mqtt.crt") | |
client.connect("mqtt.coventry.ac.uk", 8883) | |
client.loop_forever() | |