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# Mengimpor library
import pandas as pd
import streamlit as st
import pickle
# Menghilangkan warning
import warnings
warnings.filterwarnings("ignore")
# Menulis judul
st.markdown("<h1 style='text-align: center; '> Model Regresi </h1>", unsafe_allow_html=True)
st.markdown('---'*10)
# Fungsi untuk prediksi
def final_prediction(values, model):
global prediction
prediction = model.predict(values)
return prediction
# Ini merupakan fungsi utama
def main():
# Nilai awal
rd = 150000.2
adm = 140000.3
mkt = 300000.1
with st.container():
col1, col2, col3 = st.columns(3)
with col1:
rd = st.number_input('R&D', value=rd)
with col2:
adm = st.number_input('Administrasi', value=adm)
with col3:
mkt = st.number_input('Marketing', value=mkt)
st.markdown('---'*10)
wly = st.selectbox('Lokasi', ('New York', 'California', 'Florida'))
data = {
'R&D': rd,
'Administrasi': adm,
'Marketing': mkt,
'Wilayah': wly,
}
kolom = list(data.keys())
df_final = pd.DataFrame([data.values()],columns=kolom)
# load model
my_model = pickle.load(open('model_regresi_terbaik.pkl', 'rb'))
# Predict
result = round(float(final_prediction(df_final, my_model)),2)
st.markdown('---'*10)
st.write('<center><b><h3>Predicted Profit= ', result,'</b></h3>', unsafe_allow_html=True)
if __name__ == '__main__':
main()
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