Resolving Unrealistic Weather Predictions in MicroPython Linear Regression Model on Raspberry Pi Pic

import network
import urequests
import json

def connect_wifi(ssid, password):
    wlan = network.WLAN(network.STA_IF)
    wlan.active(True)
    wlan.connect(ssid, password)
    
    while not wlan.isconnected():
        print("Connecting to WiFi...")
        time.sleep(1)
    
    print("Connected to WiFi", wlan.ifconfig())

def get_weather(api_key, city):
    url = f"http://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}&units=metric"
    response = urequests.get(url)
    data = json.loads(response.text)
    temperature = data['main']['temp']
    humidity = data['main']['humidity']
    return temperature, humidity

coef_temp = 0.5
coef_humidity = -0.2
intercept = 15

def predict_next_hour(temp, humidity):
    next_temp = coef_temp * temp + coef_humidity * humidity + intercept
    return next_temp

def classify_weather(temp, humidity):
    if temp > 25 and humidity < 60:
        return "Sunny"
    elif humidity > 80:
        return "Rainy"
    else:
        return "Cloudy"

connect_wifi('********', 'xucgkudwetr,.')
api_key = '*************'
city = 'your_city'

while True:
    temp, humidity = get_weather(api_key, city)
    next_temp = predict_next_hour(temp, humidity)
    weather = classify_weather(next_temp, humidity)
    
    print(f"Current Temp: {temp}°C, Humidity: {humidity}%")
    print(f"Predicted Next Hour Temp: {next_temp}°C")
    print(f"Predicted Weather: {weather}")
    
    time.sleep(3600)