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Sigorta.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:488fcba24d1edcaa614b57b8d03c43b970e65215ed766519508b2606ae68e093
+size 4525961

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,OneHotEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.compose import ColumnTransformer
+
+df = pd.read_csv('train.csv')
+
+x = df.drop(['id', 'Response'], axis=1)
+y = df[['Response']]
+
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=42)
+
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', StandardScaler(), ['Age','Driving_License','Region_Code','Previously_Insured','Annual_Premium','Policy_Sales_Channel','Vintage']),
+        ('cat', OneHotEncoder(), ['Gender','Vehicle_Age','Vehicle_Damage'])
+    ]
+)
+
+
+# Prediction function
+def mantar_pred(Gender, Age, Driving_License, Region_Code, Previously_Insured, Vehicle_Age, Vehicle_Damage, Annual_Premium, Policy_Sales_Channel, Vintage):
+    input_data = pd.DataFrame({
+        'Gender': [Gender],
+        'Age': [Age],
+        'Driving_License': [Driving_License],
+        'Region_Code': [Region_Code],
+        'Previously_Insured': [Previously_Insured],
+        'Vehicle_Age': [Vehicle_Age],
+        'Vehicle_Damage': [Vehicle_Damage],
+        'Annual_Premium': [Annual_Premium],
+        'Policy_Sales_Channel': [Policy_Sales_Channel],
+        'Vintage': [Vintage]
+    })
+    
+    input_data_transformed = preprocessor.fit_transform(input_data)
+
+    model = joblib.load('Sigorta.pkl')
+
+    prediction = model.predict(input_data_transformed)
+    return float(prediction[0])
+
+# Streamlit UI
+def main():
+    st.title("Sigorta Poliçesi Satış Tahmini")
+    st.write("Veri Gir")
+
+    Gender = st.selectbox('Gender', df['Gender'].unique())
+    Age = st.slider('Age', int(df['Age'].min()), int(df['Age'].max()))
+    Driving_License = st.selectbox('Driving_License', df['Driving_License'].unique())
+    Region_Code = st.selectbox('Region_Code', df['Region_Code'].unique())
+    Previously_Insured = st.selectbox('Previously_Insured', df['Previously_Insured'].unique())
+    Vehicle_Age = st.selectbox('Vehicle_Age', df['Vehicle_Age'].unique())
+    Vehicle_Damage = st.selectbox('Vehicle_Damage', df['Vehicle_Damage'].unique())
+    Annual_Premium = st.slider('Annual_Premium', float(df['Annual_Premium'].min()), float(df['Annual_Premium'].max()))
+    Policy_Sales_Channel = st.selectbox('Policy_Sales_Channel', df['Policy_Sales_Channel'].unique())
+    Vintage = st.slider('Vintage', int(df['Vintage'].min()), int(df['Vintage'].max()))
+
+    if st.button('Predict'):
+        result = mantar_pred(Gender, Age, Driving_License, Region_Code, Previously_Insured, Vehicle_Age, Vehicle_Damage, Annual_Premium, Policy_Sales_Channel, Vintage)
+        st.write(f'The predicted result is: {result:.2f}')
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+streamlit
+scikit-learn
+pandas
+tensorflow

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2673b71eebc72002646253c511278dd0281b194b18bbbb7ecf5141c78b3b878b
+size 1070