Code for predicting accidents

Predictions.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()
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