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app.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split as tts
+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 OneHotEncoder, StandardScaler
+import streamlit as st
+
+# Verileri yükleyin
+df = pd.read_excel('cars.xls')
+
+# Bağımlı ve bağımsız değişkenleri ayırın
+x = df.drop('Price', axis=1)
+y = df[['Price']]
+
+# Eğitim ve test verilerine bölün
+x1, x2, y1, y2 = tts(x, y, random_state=42, test_size=0.30)
+
+# Veri ön işleme adımları
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', StandardScaler(), ['Mileage', 'Cylinder', 'Liter', 'Doors']),
+        ('cat', OneHotEncoder(handle_unknown='ignore'), ['Make', 'Model', 'Trim', 'Type'])
+    ]
+)
+
+# Pipeline oluşturma
+model = LinearRegression()
+pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('regressor', model)])
+
+# Modeli eğitme
+pipeline.fit(x1, y1)
+
+# Tahmin yapma
+predictions = pipeline.predict(x2)
+
+# Performans metrikleri hesaplama
+rmse = mean_squared_error(y2, predictions, squared=False)
+r2 = r2_score(y2, predictions)
+
+print(f'RMSE: {rmse}')
+print(f'R^2: {r2}')
+
+def price_pred(make, model, trim, mileage, type, cylinder, liter, doors, cruise, sound, leather):
+    # Girdi verilerini bir DataFrame'e dönüştür
+    input_data = pd.DataFrame({
+        'Make': [make],
+        'Model': [model],
+        'Trim': [trim],
+        'Mileage': [mileage],
+        'Type': [type],
+        'Cylinder': [cylinder],
+        'Liter': [liter],
+        'Doors': [doors],
+        'Cruise': [cruise],
+        'Sound': [sound],
+        'Leather': [leather]
+    })
+    
+    # Tahmin yapın
+    prediction = pipeline.predict(input_data)[0]
+    
+    return prediction
+
+# Streamlit arayüzü
+st.title("ML OPS Car Price Prediction :red_car:")
+st.write("Enter Car Details to predict the price")
+
+# Kullanıcı girdi alanları
+make = st.selectbox("Mrka ", df['Make'].unique())
+model = st.selectbox("Model ", df['Model'].unique())
+trim = st.selectbox("Döşeme ", df['Trim'].unique())
+mileage = st.number_input("Mil ",200,60000)
+type = st.selectbox("Tip ", df['Type'].unique())
+cylinder = st.number_input("Silindir ", min_value=0)
+liter = st.number_input("Litre ", min_value=1,step=1, max_value=8)
+doors = st.number_input("Kapı ", min_value=2, max_value=5)
+cruise = st.selectbox("Cruise", [0, 1])
+sound = st.selectbox("Sound", [0, 1])
+leather = st.selectbox("Leather", [0, 1])
+
+# Tahmin butonu
+if st.button('Predict'):
+    price = price_pred(make, model, trim, mileage, type, cylinder, liter, doors, cruise, sound, leather)
+    st.success(f'Predicted Price: ${price}')