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Sıcaklık.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:86ed1475147beff2fc7a91f1f830775728262cffd9a0349e7fcefcdcdd70df1c
+size 576486

app.py

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+import pandas as pd
+import numpy as np
+import streamlit as st
+import joblib
+
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import r2_score, mean_squared_error
+
+# Veri setini yükle
+df = pd.read_csv('train.csv')
+
+# Tarih bilgisini işleyin
+df['date'] = pd.to_datetime(df['date'])
+df['year'] = df['date'].dt.year
+df['month'] = df['date'].dt.month
+df['day'] = df['date'].dt.day
+df['hour'] = df['date'].dt.hour
+df['minute'] = df['date'].dt.minute
+
+# Özellik ve hedef değişkenleri ayırın
+x = df.drop(['id', 'date', 'Temperature'], axis=1)
+y = df[['Temperature']]  # Hedef değişken "Temperature"
+
+# Tüm sütunların sayısal olduğundan emin olun
+x = x.select_dtypes(include=[np.number])  # Yalnızca sayısal sütunları seç
+
+# Eğitim ve test setlerine ayır
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=42)
+
+# Sayısal veriler için ön işleyici
+preprocessor = StandardScaler()
+
+def time_pred(feature_AA, feature_AB, feature_BA, feature_BB, feature_CA, feature_CB, year, month, day, hour, minute):
+    input_data = pd.DataFrame({
+        'feature_AA': [feature_AA],
+        'feature_AB': [feature_AB],
+        'feature_BA': [feature_BA],
+        'feature_BB': [feature_BB],
+        'feature_CA': [feature_CA],
+        'feature_CB': [feature_CB],
+        'year': [year],
+        'month': [month],
+        'day': [day],
+        'hour': [hour],
+        'minute': [minute]
+    })
+
+    input_data_transformed = preprocessor.fit_transform(input_data)
+
+    model = joblib.load('Sıcaklık.pkl')
+
+    prediction = model.predict(input_data_transformed)
+    return float(prediction[0])
+
+
+st.title("Sıcaklık Tahmin Uygulaması")
+st.write("Veri Girin")
+
+feature_AA = st.number_input('feature_AA', min_value=-100.0, max_value=100.0, value=0.0, step=0.1)
+feature_AB = st.number_input('feature_AB', min_value=-100.0, max_value=100.0, value=0.0, step=0.1)
+feature_BA = st.number_input('feature_BA', min_value=-100.0, max_value=100.0, value=0.0, step=0.1)
+feature_BB = st.number_input('feature_BB', min_value=-100.0, max_value=100.0, value=0.0, step=0.1)
+feature_CA = st.number_input('feature_CA', min_value=-100.0, max_value=100.0, value=0.0, step=0.1)
+feature_CB = st.number_input('feature_CB', min_value=-100.0, max_value=100.0, value=0.0, step=0.1)
+year = st.number_input('Year', min_value=1900, max_value=2100, value=2024)
+month = st.number_input('Month', min_value=1, max_value=12, value=9)
+day = st.number_input('Day', min_value=1, max_value=31, value=29)
+hour = st.number_input('Hour', min_value=0, max_value=23, value=0)
+minute = st.number_input('Minute', min_value=0, max_value=59, value=0)
+
+if st.button('Tahmin Et'):
+    time = time_pred(feature_AA, feature_AB, feature_BA, feature_BB, feature_CA, feature_CB, year, month, day, hour, minute)
+    st.write(f'Tahmin edilen sıcaklık: {time:.2f} °C')

requirements.txt

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+streamlit
+scikit-learn
+pandas
+tensorflow