Upload 3 files

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+
+# Örnek veri oluşturma (gerçek verilerinizi burada kullanmalısınız)
+data = {
+    'Feature1': [1, 2, 3, 4, 5],
+    'Feature2': [2, 3, 4, 5, 6],
+    'Target': [1.5, 2.5, 3.5, 4.5, 5.5]
+}
+
+df = pd.DataFrame(data)
+X = df[['Feature1', 'Feature2']]
+y = df['Target']
+
+# Veriyi eğitim ve test setlerine ayır
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Modeli oluştur ve eğit
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Modeli kaydet
+with open('model.pkl', 'wb') as file:
+    pickle.dump(model, file)
+
+# Modeli yükle
+with open('model.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+# Streamlit uygulaması
+st.title("Lineer Regresyon Tahmin Uygulaması")
+
+# Kullanıcıdan girdi al
+st.sidebar.header("Girdi Verileri")
+feature1 = st.sidebar.number_input("Özellik 1", min_value=0.0, max_value=10.0, value=5.0)
+feature2 = st.sidebar.number_input("Özellik 2", min_value=0.0, max_value=10.0, value=5.0)
+
+# Tahmin yapma
+input_data = np.array([[feature1, feature2]])
+if st.button("Tahmin Et"):
+    prediction = model.predict(input_data)
+    st.subheader("Tahmin Edilen Değer:")
+    st.write(f"{prediction[0]:.2f}")

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5504516d5c325f82817fa83cd21c240a0a30937796d918fa9d1b3ad9b581cee7
+size 544

requirements.txt

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+streamlit
+tensorflow
+opencv-python
+scikit-learn