Predictions.py

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()