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logistic_regression_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ee7c783d2e76ee847a7b759f79f0505537185daf4d5105085c4a8610def7d81d
+size 886

model.py

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