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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+insurance_fraud_decetion_doc.docx filter=lfs diff=lfs merge=lfs -text
+insurance_fraud_decetion_notebook.ipynb filter=lfs diff=lfs merge=lfs -text

app.py

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+import streamlit as st 
+import joblib 
+
+# models
+
+scaler=joblib.load('scaler_model.joblib')
+
+insurance_model=joblib.load('insurance_fraud_model.joblib')
+
+## Heading
+html_temp = """
+    <div style="background-color:tomato;padding:10px">
+    <h2 style="color:Black;text-align:center;">Insurance Claims Analysis   </h2>
+    </div>
+    """
+st.markdown(html_temp, unsafe_allow_html=True)
+
+## inputs
+
+## 'incident_cause'
+option_incident=['Driver error',"Crime","Other driver error",'Natural causes','Other causes']
+incident_case=st.selectbox("Select the cause of incident :",options=option_incident)
+incident_values=option_incident.index(incident_case)
+
+## 'claim_area'
+
+option_claim_area=["Auto","Home"]
+claim_area=st.selectbox("Select the Claim Area: ",option_claim_area)
+claim_area_value=option_claim_area.index(claim_area)
+
+## 'police_report'
+option_police_report=['NO',"Unknow","Yes"]
+police_report=st.selectbox("You have Given Police Report:",option_police_report)
+police_report_value=option_police_report.index(police_report)
+
+## 'claim_type'
+option_claim_type=["Material only ","Injury only","Material and injury"]
+claim_type=st.selectbox("Select the Type of Claim :",option_claim_type)
+claim_type_value=option_claim_type.index(claim_type)
+
+##  'claim_amount'
+claim_amount=st.slider("Slide the Claim Amount:",min_value=1000,max_value=48150)
+
+## 'total_policy_claims'
+policy_claims=st.slider("Slide the No of Policies Claimed:",min_value=0,max_value=8)
+
+if st.button("Submit"):
+    # st.write(incident_values,claim_area_value,police_report_value,claim_area_value,claim_amount,policy_claims)
+    values=scaler.transform([[incident_values,claim_area_value,police_report_value,claim_area_value,claim_amount,policy_claims]])
+    #st.write(values)
+    output=insurance_model.predict(values)
+    if output==0:
+        st.write("The Transaction is Not Faurd")
+    else:
+        st.write("The Transaction is Faurd")

insurance_fraud_decetion_doc.docx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e83c6ddf323e01c7c2b232a12c0900972555ea6936aa39bf1ef498e85a74fccd
+size 441253

insurance_fraud_decetion_notebook.ipynb

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+oid sha256:4864b4518d56b434602899dd71165ec728592d2b03e90628b9fe377352bbc61c
+size 11283942

insurance_fraud_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6cd55fb747e88484cd822e7a4ec17f86d41e0900ed5b6d45ba8b6ae2543c665e
+size 186589

requirements.txt

@@ -0,0 +1,9 @@
+joblib==1.2.0
+matplotlib==3.7.1
+matplotlib-inline==0.1.6
+numpy==1.26.4
+pandas==1.5.3
+scikit-learn==1.6.0
+streamlit==1.41.1
+tabulate==0.8.10
+imblearn==0.13.

scaler_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9570d6c2a7b3f18f15c95e7165a27258965ec324fe835198fe7c602268fe49e6
+size 1319