:smiley: German Price Prediction Commit

M2P1_pred.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:726bfe25a1d965b190b3d8225f4fb45eb38a36b6992b5a29e3506ff410817b61
+size 5642

app.py

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+import streamlit as st
+import feature_engine
+import pandas as pd
+import pickle
+
+st.title("Used car sales price prediction")
+
+# import model
+model = pickle.load(open("M2P1_pred.pkl", "rb"))
+
+st.write('Insert features First:')
+
+# user input
+odometer = st.slider(label='odometer', min_value=0, max_value=150000, step=1)
+powerPS = st.slider(label='powerPS', min_value=1, max_value=923, step=1)
+yearOfRegistration = st.slider(label='yearOfRegistration', min_value=1863, max_value=2016, step=1)
+gearbox = st.selectbox(label='gearbox', options=['automatik', 'manuell'])
+models = st.selectbox(label='model', options=['7er', 'golf', 'a3', 'scirocco', 'e_klasse', 'c_klasse', 'a1',
+       'a_klasse', 's_klasse', 'passat', 'corsa', '3er', '1er', '5er',
+       'a6', 'a4', 'transporter', 'vito', '100', 'm_klasse', 'lupo',
+       'touareg', 'andere', 'touran', 'x_reihe', 'tigra', 'signum',
+       'sharan', 'a5', 'beetle', 'phaeton', 'sl', 'insignia', 'up', '80',
+       'z_reihe', 'clk', 'vivaro', 'tiguan', 'sprinter', 'astra', 'viano',
+       'bora', 'fox', 'polo', 'zafira', 'meriva', 'vectra', 'omega', 'a8',
+       'caddy', 'tt', 'eos', 'slk', 'm_reihe', 'glk', 'combo', 'a2',
+       'b_klasse', 'cc', 'v_klasse', 'jetta', 'q7', 'cl', '90', 'q3',
+       'q5', 'agila', 'calibra', 'kaefer', 'gl', 'amarok', 'antara',
+       'kadett', '6er', 'g_klasse', '200'])
+fuelType = st.selectbox(label='fuelType', options=['benzin', 'diesel', 'lpg', 'cng', 'andere', 'hybrid', 'elektro'])
+brand = st.selectbox(label='brand', options=['volkswagen', 'audi', 'opel', 'mercedes_benz', 'bmw'])
+
+
+# convert into dataframe
+data = pd.DataFrame({'odometer': [odometer],
+                'powerPS': [powerPS],
+                'yearOfRegistration': [yearOfRegistration],
+                'gearbox':[gearbox],
+                'model': [models],
+                'fuelType': [fuelType],
+                'brand': [brand],
+                    })
+data
+# model predict
+clas = model.predict(data).tolist()[0]
+
+#Intepretation
+if st.button('Predict'):
+    st.write('The used car price is : ', clas, 'USD')  
+
+

requirements.txt

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+streamlit
+pandas
+scikit-learn==1.1.2
+feature_engine==1.5.1