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app.py

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+import pandas as pd
+import numpy as np
+import pickle
+import streamlit as st
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+
+# Veri Setini Yükleyin
+df = pd.read_csv('kc_house_data.csv')
+
+# Girdileri ve çıktıları ayırma
+X = df.drop('price', axis=1)
+y = df[['price']]
+
+# Eğitim ve test setlerine ayırma
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Ön işleme ve model pipeline'ı oluşturma
+preprocessor = ColumnTransformer(transformers=[
+    ('num', StandardScaler(), ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15', 'sqft_lot15']),
+    ('cat', OneHotEncoder(), ['zipcode'])
+])
+
+model = LinearRegression()
+pipe = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])
+pipe.fit(X_train, y_train)
+
+# Modeli kaydet
+with open('ev_fiyat_modeli.pkl', 'wb') as f:
+    pickle.dump(pipe, f)
+
+# Modeli yükle
+with open('ev_fiyat_modeli.pkl', 'rb') as f:
+    loaded_model = pickle.load(f)
+
+# Tahmin fonksiyonu
+def predict_price(bedrooms, bathrooms, sqft_living, sqft_lot, floors, sqft_above, sqft_basement, yr_built, yr_renovated, sqft_living15, sqft_lot15, zipcode):
+    input_data = pd.DataFrame({
+        'bedrooms': [bedrooms],
+        'bathrooms': [bathrooms],
+        'sqft_living': [sqft_living],
+        'sqft_lot': [sqft_lot],
+        'floors': [floors],
+        'sqft_basement': [sqft_basement],
+        'sqft_above': [sqft_above],}
+        'yr_built': [yr_built],
+        'yr_renovated': [yr_renovated],
+        'sqft_living15': [sqft_living15],
+        'sqft_lot15': [sqft_lot15],
+        'zipcode': [zipcode]
+    })
+    prediction = loaded_model.predict(input_data)[0][0]
+    return max(0, prediction)  # Negatif değerleri sıfıra eşitle
+
+# Streamlit arayüzü
+st.title("Home Price Estimate")
+st.write("Enter the features of the house")
+
+bedrooms = st.number_input("Number of Attack Rooms", 1, 10)
+bathrooms = st.number_input("Number of Bathrooms", 1, 5)
+sqft_living = st.number_input("Metrekare (Yaşam Alanı)", 50, 10000)
+sqft_lot = st.number_input("Square Meters (Living Space)", 50, 50000)
+floors = st.number_input("Number of Floors", 1, 3)
+sqft_above = st.number_input("Square Meters (Above Ground)", 50, 10000)
+sqft_basement = st.number_input("Square Meters (Underground)", 0, 5000)
+yr_built = st.number_input("Year of Construction", 1900, 2022)
+yr_renovated = st.number_input("Year of Renovation", 0, 2022)
+sqft_living15 = st.number_input("Nearby Square Meters (Living Space)", 50, 10000)
+sqft_lot15 = st.number_input("Nearby Square Meters (Land Area)", 50, 50000)
+zipcode = st.selectbox("Postal code", df['zipcode'].unique())
+
+if st.button("Set an estimated price"):
+    pred = predict_price(bedrooms, bathrooms, sqft_living, sqft_lot, floors, sqft_above, sqft_basement, yr_built, yr_renovated, sqft_living15, sqft_lot15, zipcode)
+    st.write("Estimated Price: $", round(pred, 2))

ev_fiyat_modeli.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:09ac548111585d3787d50bc50c056ccaf21841fdb7f24a504a55eaed124dea9d
+size 3887

requirements.txt

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+pandas
+numpy
+scikit-learn
+streamlit