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Browse files- logistic_regression_model.pkl +3 -0
- model.py +36 -0
logistic_regression_model.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:ee7c783d2e76ee847a7b759f79f0505537185daf4d5105085c4a8610def7d81d
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size 886
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model.py
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import streamlit as st
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import pickle
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import pandas as pd
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st.title("BEHAIVER PREDICTION")
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# Load the trained model
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with open(r'C:\Users\hp\Desktop\project\model\accused behaiver\logistic_regression_model.pkl', 'rb') as f:
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model = pickle.load(f)
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# Create input fields
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age = st.number_input("Enter age:", min_value=0)
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sex = st.radio("Select sex:", ("FEMALE", "MALE"))
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present_city = st.radio("Select present city:", ("Bengaluru City", "Other"))
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present_state = st.radio("Select present state:", ("Karnataka", "Other"))
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# Convert inputs to model format
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sex_female = 1 if sex == 'FEMALE' else 0
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sex_male = 1 if sex == 'MALE' else 0
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city_bengaluru = 1 if present_city == 'Bengaluru City' else 0
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state_karnataka = 1 if present_state == 'Karnataka' else 0
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# Create a data frame for the input data
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input_data = pd.DataFrame({
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'age': [age],
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'Sex_FEMALE': [sex_female],
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'Sex_MALE': [sex_male],
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'PresentCity_Bengaluru City': [city_bengaluru],
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'PresentState_Karnataka': [state_karnataka]
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})
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# Make a prediction
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if st.button("Predict Behavioral Status"):
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prediction = model.predict(input_data)
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st.write(f"The predicted Behavioral Status is: {prediction[0]}")
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