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KNeighborsClassifier.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b23c449856ea40d6f8e4cfc1e98a2d65697612e7bdfdf0d65fe7743e7f3c1d60
+size 59894

app.py

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+import pandas as pd
+import gradio as gr
+import joblib
+
+
+model=joblib.load('KNeighborsClassifier.pkl')
+
+
+
+def Students_Performance_Prediction_Model(Q,A,ME,ASS,Ag,FE,CG,T):
+    try:
+        input_data=pd.DataFrame({
+            'Quiz01 [10]':[Q],
+            'Assignment01 [8]':[A],
+            'Midterm Exam [20]':[ME],
+            'Assignment02 [12]':[ASS],
+            'Assignment03 [25]':[Ag],
+            'Final Exam [35]':[FE],
+            'Course Grade':[CG],
+            'Total [100]':[T]
+        })
+        prediction=model.predict(input_data)
+        if prediction[0]==0:
+            return 'G'
+        else:
+            return 'W'
+    except Exception as e:
+        return str(e)
+gr.Interface(
+    inputs=[
+        gr.Number(label='Quiz01 [10]'),
+        gr.Number(label='Assignment01 [8]'),
+        gr.Number(label='Midterm Exam [20]'),
+        gr.Number(label='Assignment02 [12]'),
+        gr.Number(label='Assignment03 [25]'),
+        gr.Number(label='Final Exam [35]'),
+        gr.Number(label='Course Grade'),
+        gr.Number(label='Total [100]')
+        
+    ],
+    fn=Students_Performance_Prediction_Model,
+    outputs=gr.Textbox(label='Prediction Risk'),
+    title='prediction Program',
+    description='This program for predict Score Risk of Students'
+).launch()

requirments.txt

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+pandas
+numpy
+scikit-learn==1.0.2
+joblib
+gradio