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‎Prediction Models/Ashtma Attack Prediction/app/Asthma Risk Prediction.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "be5b3365",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['PEFR_predictor.joblib']"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"from sklearn.tree import DecisionTreeClassifier as dtc\n",
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"import joblib\n",
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"\n",
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"data=pd.read_csv(\"PEFR_Data_set.csv\")\n",
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"data.shape\n",
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"X=data.drop(columns=['Age','Height','PEFR'])\n",
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"y=data['PEFR']\n",
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"model=dtc()\n",
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"model.fit(X,y)\n",
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"joblib.dump(model, 'PEFR_predictor.joblib')\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "39c29fa3",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Enter Gender 1-Male 0-Female1\n",
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"Enter Temperature C:22\n",
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"Enter Humidity %:33\n",
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"Enter PM 2.5 Value:44\n",
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"Enter PM 10 Value:22\n",
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"Enter Actual PEFR value334\n",
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"512\n",
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"65.234375\n",
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"MODERATE\n"
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]
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}
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],
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"source": [
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"\n",
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"model = joblib.load('PEFR_predictor.joblib')\n",
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"g=int(input(\"Enter Gender 1-Male 0-Female\"))\n",
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"p=float(input('Enter Temperature C:'))\n",
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"q=float(input('Enter Humidity %:'))\n",
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"r=float(input('Enter PM 2.5 Value:'))\n",
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"s=float(input('Enter PM 10 Value:'))\n",
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"prediction = model.predict([[g,p,q,r,s]])\n",
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"predicted_pefr = prediction[0]\n",
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"actual_pefr = float(input(\"Enter Actual PEFR value\"))\n",
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"print(predicted_pefr)\n",
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"perpefr = (actual_pefr/predicted_pefr)*100\n",
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"print(perpefr)\n",
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"if perpefr >= 80:\n",
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" print('SAFE')\n",
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"elif perpefr >= 50:\n",
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" print('MODERATE')\n",
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"else:\n",
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" print('RISK')\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "66e1edfe",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Prediction Models/Ashtma Attack Prediction/app/Asthma Risk Prediction.py‎

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#!/usr/bin/env python
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# coding: utf-8
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# In[1]:
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import pandas as pd
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from sklearn.tree import DecisionTreeClassifier as dtc
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import joblib
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import requests
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from bs4 import BeautifulSoup
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# In[2]:
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data=pd.read_csv("PEFR_Data_set.csv")
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data.shape
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X=data.drop(columns=['Age','Height','PEFR'])
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y=data['PEFR']
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model=dtc()
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model.fit(X,y)
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#joblib.dump(model, 'PEFR_predictor.joblib')
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# In[3]:
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#model = joblib.load('PEFR_predictor.joblib')
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# In[5]:
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city = input("Enter City:")
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url = f'https://www.iqair.com/in-en/india/tamil-nadu/{city}'
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r = requests.get(url)
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soup = BeautifulSoup(r.content,'html.parser')
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aqi_dict = []
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s = soup.find_all(class_ = "mat-tooltip-trigger pollutant-concentration-value")
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for x in s:
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aqi_dict.append(x.text)
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pm2 = aqi_dict[0]
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pm10 = aqi_dict[1]
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t = soup.find('div', class_="weather__detail")
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y = t.text
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temp_index = y.find('Temperature')+11
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degree_index = y.find('°')
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temp = y[temp_index : degree_index]
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hum_index = y.find('Humidity')+8
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perc_index = y.find('%')
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hum = y[hum_index:perc_index]
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'''
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print(pm2)
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print(pm10)
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print(temp)
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print(hum)
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'''
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# In[6]:
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g=int(input("Enter Gender (1-Male/0-Female): "))
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p=temp
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q=hum
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r=pm2
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s=pm10
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prediction = model.predict([[g,p,q,r,s]])
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predicted_pefr = prediction[0]
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actual_pefr = float(input("Enter Actual PEFR value: "))
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perpefr = (actual_pefr/predicted_pefr)*100
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if perpefr >= 80:
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print('SAFE')
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elif perpefr >= 50:
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print('MODERATE')
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else:
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print('RISK')
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‎Prediction Models/Ashtma Attack Prediction/app/main.py‎

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from kivymd.app import MDApp
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from kivymd.uix.screen import Screen
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from kivymd.uix.label import MDLabel
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from kivymd.uix.button import MDRectangleFlatButton
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from kivy.lang import Builder
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from kivy.core.window import Window
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import joblib
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#Window.size=(320,600)
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username_input="""
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MDTextField:
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hint_text: "Enter Gender 1-Male 0-Female: "
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hint_text_size:30
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helper_text: "must be an integer"
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helper_text_mode: "on_focus"
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icon_right: "language-python"
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icon_right_color: app.theme_cls.primary_color
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pos_hint:{'center_x': 0.5, 'center_y': 0.8}
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size_hint_x:.5
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size_hint_y:.1
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font_size:'20sp'
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width:400
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"""
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username_input1 = """
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MDTextField:
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hint_text: "Enter Temperature C: "
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hint_text_size:30
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helper_text: "must be an integer"
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helper_text_mode: "on_focus"
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icon_right: "android"
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icon_right_color: app.theme_cls.primary_color
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pos_hint:{'center_x': 0.5, 'center_y': 0.7}
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size_hint_x:.5
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size_hint_y:.1
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font_size:'20sp'
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width:400
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"""
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username_input2 = """
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MDTextField:
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hint_text: "Enter Humidity %: "
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hint_text_size:30
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helper_text: "must be an integer"
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helper_text_mode: "on_focus"
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icon_right: "android"
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icon_right_color: app.theme_cls.primary_color
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pos_hint:{'center_x': 0.5, 'center_y': 0.6}
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size_hint_x:.5
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size_hint_y:.1
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font_size:'20sp'
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width:400
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"""
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username_input3 = """
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MDTextField:
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hint_text: "Enter PM 2.5 Value: "
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hint_text_size:30
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helper_text: "must be an integer"
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helper_text_mode: "on_focus"
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icon_right: "android"
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icon_right_color: app.theme_cls.primary_color
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pos_hint:{'center_x': 0.5, 'center_y': 0.5}
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size_hint_x:.5
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size_hint_y:.1
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font_size:'20sp'
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width:400
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"""
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username_input4 = """
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MDTextField:
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hint_text: "Enter PM 10 Value: "
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hint_text_size:30
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helper_text: "must be an integer"
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helper_text_mode: "on_focus"
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icon_right: "android"
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icon_right_color: app.theme_cls.primary_color
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pos_hint:{'center_x': 0.5, 'center_y': 0.4}
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size_hint_x:.5
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size_hint_y:.1
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font_size:'20sp'
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width:400
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"""
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username_input5 = """
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MDTextField:
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hint_text: "Enter Actual PEFR value: "
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hint_text_size:30
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helper_text: "must be an integer"
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helper_text_mode: "on_focus"
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icon_right: "android"
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icon_right_color: app.theme_cls.primary_color
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pos_hint:{'center_x': 0.5, 'center_y': 0.3}
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size_hint_x:.5
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size_hint_y:.1
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font_size:'20sp'
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width:400
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"""
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def predicter(g,p,q,r,s,actual_pefr):
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model = joblib.load('decision_tree_model.joblib')
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prediction = model.predict([[g,p,q,r,s]])
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predicted_pefr = prediction[0]
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print(predicted_pefr)
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perpefr = (actual_pefr/predicted_pefr)*100
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print(perpefr)
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if perpefr >= 80:
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print('SAFE')
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re='SAFE'
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elif perpefr >= 50:
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print('MODERATE')
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re='MODERATE'
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else:
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print('RISK')
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re='RISK'
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return (re,predicted_pefr,actual_pefr,(perpefr//100)*10)
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class DemoApp(MDApp):
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def build(self):
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self.screen=Screen()
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self.theme_cls.primary_palette = "Green"
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self.label = MDLabel(text="ASTHMA RISK PREDICTION", halign="center",theme_text_color='Custom',
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text_color=(0,1,0,1),font_style='H4',pos_hint={'center_x': 0.5, 'center_y': 0.9})
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self.username = Builder.load_string(username_input)
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self.username1 = Builder.load_string(username_input1)
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self.username2 = Builder.load_string(username_input2)
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self.username3 = Builder.load_string(username_input3)
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self.username4 = Builder.load_string(username_input4)
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self.username5 = Builder.load_string(username_input5)
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self.btn = MDRectangleFlatButton(text='Calculate',font_size='20sp',
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pos_hint={'center_x': 0.5, 'center_y': 0.1},size_hint=(.2, .1)
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,on_release=self.mul)
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self.screen.add_widget(self.label)
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self.screen.add_widget(self.username)
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self.screen.add_widget(self.username1)
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self.screen.add_widget(self.username2)
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self.screen.add_widget(self.username3)
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self.screen.add_widget(self.username4)
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self.screen.add_widget(self.username5)
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self.screen.add_widget(self.btn)
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return self.screen
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def mul(self,obj):
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self.screen.remove_widget(self.label)
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self.screen.remove_widget(self.username)
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self.screen.remove_widget(self.username1)
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self.screen.remove_widget(self.username2)
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self.screen.remove_widget(self.username3)
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self.screen.remove_widget(self.username4)
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self.screen.remove_widget(self.username5)
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self.screen.remove_widget(self.btn)
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r=predicter(float(self.username.text),
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float(self.username1.text),
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float(self.username2.text),
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float(self.username3.text),
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float(self.username4.text),
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float(self.username5.text))
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self.label1 = MDLabel(text="PREDICTION RESULTS", halign="center",theme_text_color='Custom',
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text_color=(0,1,0,1),font_style='H4',pos_hint={'center_x': 0.5, 'center_y': 0.9})
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self.label_1 = MDLabel(text="PREDICTED PEFR: "+str(r[1]), halign="center",theme_text_color='Custom',
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text_color=(0,0,1,1),font_style='H5',pos_hint={'center_x': 0.5, 'center_y': 0.7})
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self.label1_ = MDLabel(text="ACTUAL PEFR ENTERED: "+str(r[2]), halign="center",theme_text_color='Custom',
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text_color=(1,0,1,1),font_style='H5',pos_hint={'center_x': 0.5, 'center_y': 0.5})
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if(r[0]=='SAFE'):
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c=(0,1,0,1)
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if(r[0]=='MODERATE'):
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c=(1,1,0,1)
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if(r[0]=='RISK'):
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c=(1,0,0,1)
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self.label2 = MDLabel(text=str(r[0]), halign="center",theme_text_color='Custom',
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text_color=c,font_style='H4',pos_hint={'center_x': 0.5, 'center_y': 0.3})
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self.button = MDRectangleFlatButton(text='Back',
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pos_hint={'center_x': 0.8, 'center_y': 0.1 },
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on_release=self.back)
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self.screen.add_widget(self.label1)
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self.screen.add_widget(self.label_1)
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self.screen.add_widget(self.label1_)
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self.screen.add_widget(self.label2)
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self.screen.add_widget(self.button)
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def back(self,obj):
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self.screen.add_widget(self.label)
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self.screen.add_widget(self.username)
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self.screen.add_widget(self.username1)
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self.screen.add_widget(self.username2)
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self.screen.add_widget(self.username3)
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self.screen.add_widget(self.username4)
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self.screen.add_widget(self.username5)
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self.screen.add_widget(self.btn)
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self.screen.remove_widget(self.button)
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self.screen.remove_widget(self.label1)
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self.screen.remove_widget(self.label2)
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self.screen.remove_widget(self.label_1)
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self.screen.remove_widget(self.label1_)
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self.screen.remove_widget(self.button)
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DemoApp().run()

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