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import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
import streamlit as st
df = pd.read_excel('cars.xls')
x = df.drop('Price', axis=1)
y = df[['Price']]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=42)
preprocessor = ColumnTransformer(
transformers=[
('num', StandardScaler(), ['Mileage', 'Cylinder', 'Liter', 'Doors']),
('cat', OneHotEncoder(), ['Make', 'Model', 'Trim', 'Type'])
]
)
model = LinearRegression()
pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('regressor', model)])
pipeline.fit(x_train, y_train)
pred = pipeline.predict(x_test)
rmse = mean_squared_error(pred, y_test) ** 0.5
r2 = r2_score(pred, y_test)
def price_pred(make, model, trim, mileage, car_type, cylinder, liter, doors, cruise, sound, leather):
input_data = pd.DataFrame({'Make': [make],
'Model': [model],
'Trim': [trim],
'Mileage': [mileage],
'Type': [car_type],
'Cylinder': [cylinder],
'Liter': [liter],
'Doors': [doors],
'Cruise': [cruise],
'Sound': [sound],
'Leather': [leather]})
prediction = pipeline.predict(input_data)[0]
return prediction
def main():
st.title('Car Price Prediction :red_car:')
st.write('Enter Car Details to predict the price')
make = st.selectbox('Make', df['Make'].unique())
models = df[df['Make'] == make]['Model'].unique()
model = st.selectbox('Model', models)
trims = df[(df['Make'] == make) & (df['Model'] == model)]['Trim'].unique()
trim = st.selectbox('Trim', trims)
mileage = st.number_input('Mileage', 200, 60000)
car_type = st.selectbox('Type', df['Type'].unique())
cylinder = st.selectbox('Cylinder', df['Cylinder'].unique())
liter = st.number_input('Liter', 1, 6)
doors = st.selectbox('Doors', df['Doors'].unique())
cruise = st.radio('Cruise', [0, 1])
sound = st.radio('Sound', [0, 1])
leather = st.radio('Leather', [0, 1])
if st.button('Predict'):
price = price_pred(make, model, trim, mileage, car_type, cylinder, liter, doors, cruise, sound, leather)
price = float(price) # NumPy ndarray'ini float türüne dönüştürme
st.write(f'The Predicted Price is: ${price:.2f}')
if __name__ == '__main__':
main() |