5151y = df .pop ("AQI" )
5252
5353
54- x_train , x_test , y_train , y_test = train_test_split (df , y , test_size = 0.2 , random_state = 0 )
54+ x_train , x_test , y_train , y_test = train_test_split (
55+ df , y , test_size = 0.2 , random_state = 0 )
5556
5657
5758model = RandomForestRegressor (max_depth = 50 , random_state = 0 )
5859model .fit (x_train , y_train )
5960
61+ 6062def main ():
6163 st .title ("Air Quality Index Prediction" )
6264
6365 st .write ("## User Input Features" )
6466
65- 6667 city = st .selectbox ("City" , df ["City" ].unique ())
67- pm2_5 = st .slider ("PM2.5" , float (df ["PM2.5" ].min ()), float (df ["PM2.5" ].max ()), float (df ["PM2.5" ].mean ()))
68- pm10 = st .slider ("PM10" , float (df ["PM10" ].min ()), float (df ["PM10" ].max ()), float (df ["PM10" ].mean ()))
69- no = st .slider ("NO" , float (df ["NO" ].min ()), float (df ["NO" ].max ()), float (df ["NO" ].mean ()))
70- no2 = st .slider ("NO2" , float (df ["NO2" ].min ()), float (df ["NO2" ].max ()), float (df ["NO2" ].mean ()))
71- nox = st .slider ("NOx" , float (df ["NOx" ].min ()), float (df ["NOx" ].max ()), float (df ["NOx" ].mean ()))
72- nh3 = st .slider ("NH3" , float (df ["NH3" ].min ()), float (df ["NH3" ].max ()), float (df ["NH3" ].mean ()))
73- co = st .slider ("CO" , float (df ["CO" ].min ()), float (df ["CO" ].max ()), float (df ["CO" ].mean ()))
74- so2 = st .slider ("SO2" , float (df ["SO2" ].min ()), float (df ["SO2" ].max ()), float (df ["SO2" ].mean ()))
75- o3 = st .slider ("O3" , float (df ["O3" ].min ()), float (df ["O3" ].max ()), float (df ["O3" ].mean ()))
76- benzene = st .slider ("Benzene" , float (df ["Benzene" ].min ()), float (df ["Benzene" ].max ()), float (df ["Benzene" ].mean ()))
77- toluene = st .slider ("Toluene" , float (df ["Toluene" ].min ()), float (df ["Toluene" ].max ()), float (df ["Toluene" ].mean ()))
78- xylene = st .slider ("Xylene" , float (df ["Xylene" ].min ()), float (df ["Xylene" ].max ()), float (df ["Xylene" ].mean ()))
79- 80- 68+ pm2_5 = st .slider ("PM2.5" , float (df ["PM2.5" ].min ()), float (
69+ df ["PM2.5" ].max ()), float (df ["PM2.5" ].mean ()))
70+ pm10 = st .slider ("PM10" , float (df ["PM10" ].min ()), float (
71+ df ["PM10" ].max ()), float (df ["PM10" ].mean ()))
72+ no = st .slider ("NO" , float (df ["NO" ].min ()), float (
73+ df ["NO" ].max ()), float (df ["NO" ].mean ()))
74+ no2 = st .slider ("NO2" , float (df ["NO2" ].min ()), float (
75+ df ["NO2" ].max ()), float (df ["NO2" ].mean ()))
76+ nox = st .slider ("NOx" , float (df ["NOx" ].min ()), float (
77+ df ["NOx" ].max ()), float (df ["NOx" ].mean ()))
78+ nh3 = st .slider ("NH3" , float (df ["NH3" ].min ()), float (
79+ df ["NH3" ].max ()), float (df ["NH3" ].mean ()))
80+ co = st .slider ("CO" , float (df ["CO" ].min ()), float (
81+ df ["CO" ].max ()), float (df ["CO" ].mean ()))
82+ so2 = st .slider ("SO2" , float (df ["SO2" ].min ()), float (
83+ df ["SO2" ].max ()), float (df ["SO2" ].mean ()))
84+ o3 = st .slider ("O3" , float (df ["O3" ].min ()), float (
85+ df ["O3" ].max ()), float (df ["O3" ].mean ()))
86+ benzene = st .slider ("Benzene" , float (df ["Benzene" ].min ()), float (
87+ df ["Benzene" ].max ()), float (df ["Benzene" ].mean ()))
88+ toluene = st .slider ("Toluene" , float (df ["Toluene" ].min ()), float (
89+ df ["Toluene" ].max ()), float (df ["Toluene" ].mean ()))
90+ xylene = st .slider ("Xylene" , float (df ["Xylene" ].min ()), float (
91+ df ["Xylene" ].max ()), float (df ["Xylene" ].mean ()))
92+ 8193 input_data = pd .DataFrame (
8294 {
8395 "City" : [city ],
@@ -98,7 +110,7 @@ def main():
98110
99111 st .sidebar .write ("## City Label Mapping" )
100112 st .sidebar .write (city_mapping )
101-
113+ 102114 prediction = model .predict (input_data )
103115
104116 st .write ("## Prediction" )
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