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Abalone.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e8c3c068ccd32bfbd55550c013b4d1d0664fae8079f0c2d15379032b5701589b
+size 1914064

app.py

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+import pandas as pd
+import streamlit as st
+import joblib
+
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.compose import ColumnTransformer
+
+# Veriyi yükleme ve sütun isimlerini güncelleme
+df = pd.read_csv('train.csv')
+df.columns = df.columns.str.replace(r'[\s\.]', '_', regex=True)
+
+# Bağımlı ve bağımsız değişkenlerin seçimi
+x = df.drop(['id', 'Rings'], axis=1)
+y = df[['Rings']]
+
+# Eğitim ve test verilerini ayırma
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=42)
+
+# Ön işleme (StandardScaler ve OneHotEncoder)
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', StandardScaler(), ['Length', 'Diameter', 'Height', 'Whole_weight', 'Whole_weight_1', 'Whole_weight_2', 'Shell_weight']),
+        ('cat', OneHotEncoder(), ['Sex'])
+    ]
+)
+
+# Streamlit uygulaması
+def rings_pred(Sex, Length, Diameter, Height, Whole_weight, Whole_weight_1, Whole_weight_2, Shell_weight):
+    input_data = pd.DataFrame({
+        'Sex': [Sex],
+        'Length': [Length],
+        'Diameter': [Diameter],
+        'Height': [Height],
+        'Whole_weight': [Whole_weight],
+        'Whole_weight_1': [Whole_weight_1],
+        'Whole_weight_2': [Whole_weight_2],
+        'Shell_weight': [Shell_weight]
+    })
+    
+    
+    input_data_transformed = preprocessor.fit_transform(input_data)
+
+    model = joblib.load('Abalone.pkl')
+
+    prediction = model.predict(input_data_transformed)
+    return float(prediction[0])
+
+st.title("Abalone Veri seti ile Yaş Tahmini Regresyon Modeli")
+st.write("Veri Gir")
+
+Sex = st.selectbox('Sex', df['Sex'].unique())
+Length = st.selectbox('Length', df['Length'].unique())
+Diameter = st.selectbox('Diameter', df['Diameter'].unique())
+Height = st.selectbox('Height', df['Height'].unique())
+Whole_weight = st.selectbox('Whole_weight', df['Whole_weight'].unique())
+Whole_weight_1 = st.selectbox('Whole_weight_1', df['Whole_weight_1'].unique())
+Whole_weight_2 = st.selectbox('Whole_weight_2', df['Whole_weight_2'].unique())
+Shell_weight = st.selectbox('Shell_weight', df['Shell_weight'].unique())
+    
+if st.button('Predict'):
+    rings = rings_pred(Sex, Length, Diameter, Height, Whole_weight, Whole_weight_1, Whole_weight_2, Shell_weight)
+    st.write(f'The predicted rings is: {rings:.2f}')

requirements.txt

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+streamlit
+scikit-learn
+pandas
+tensorflow