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Akademi.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:32daa9ae4d87ba415bd99d15230a102b0b9bbcfe7ed11dff32d0883474a2d616
+size 666579

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+
+model = pickle.load(open('Akademi.pkl', 'rb'))
+scaler = pickle.load(open('scaler.pkl', 'rb'))
+
+st.title("Academy Prediction")
+
+marital_status = st.selectbox("Marital Status", options=["Married", "Single", "Divorced"])
+application_mode = st.selectbox("Application Mode", options=["Online", "Offline"])
+application_order = st.number_input("Application Order", min_value=1)
+course = st.number_input("Course", min_value=1)
+attendance = st.selectbox("Daytime/Evening Attendance", options=["Daytime", "Evening"])
+previous_qualification = st.selectbox("Previous Qualification", options=["None", "High School", "Bachelor", "Master"])
+previous_qualification_grade = st.number_input("Previous Qualification Grade", min_value=0.0)
+nationality = st.selectbox("Nationality", options=["National", "International"])
+mother_qualification = st.selectbox("Mother's Qualification", options=["None", "High School", "Bachelor", "Master"])
+father_qualification = st.selectbox("Father's Qualification", options=["None", "High School", "Bachelor", "Master"])
+mother_occupation = st.selectbox("Mother's Occupation", options=["Unemployed", "Employed"])
+father_occupation = st.selectbox("Father's Occupation", options=["Unemployed", "Employed"])
+admission_grade = st.number_input("Admission Grade", min_value=0.0)
+displaced = st.selectbox("Displaced", options=["No", "Yes"])
+educational_special_needs = st.selectbox("Educational Special Needs", options=["No", "Yes"])
+debtor = st.selectbox("Debtor", options=["No", "Yes"])
+tuition_fees_up_to_date = st.selectbox("Tuition Fees Up to Date", options=["No", "Yes"])
+gender = st.selectbox("Gender", options=["Male", "Female"])
+scholarship_holder = st.selectbox("Scholarship Holder", options=["No", "Yes"])
+age_at_enrollment = st.number_input("Age at Enrollment", min_value=0)
+international = st.selectbox("International", options=["No", "Yes"])
+curricular_units_1st_sem_credited = st.number_input("Curricular Units 1st Sem (Credited)", min_value=0)
+curricular_units_1st_sem_enrolled = st.number_input("Curricular Units 1st Sem (Enrolled)", min_value=0)
+curricular_units_1st_sem_evaluations = st.number_input("Curricular Units 1st Sem (Evaluations)", min_value=0)
+curricular_units_1st_sem_approved = st.number_input("Curricular Units 1st Sem (Approved)", min_value=0)
+curricular_units_1st_sem_grade = st.number_input("Curricular Units 1st Sem (Grade)", min_value=0.0)
+curricular_units_1st_sem_without_evaluations = st.number_input("Curricular Units 1st Sem (Without Evaluations)", min_value=0)
+curricular_units_2nd_sem_credited = st.number_input("Curricular Units 2nd Sem (Credited)", min_value=0)
+curricular_units_2nd_sem_enrolled = st.number_input("Curricular Units 2nd Sem (Enrolled)", min_value=0)
+curricular_units_2nd_sem_evaluations = st.number_input("Curricular Units 2nd Sem (Evaluations)", min_value=0)
+curricular_units_2nd_sem_approved = st.number_input("Curricular Units 2nd Sem (Approved)", min_value=0)
+curricular_units_2nd_sem_grade = st.number_input("Curricular Units 2nd Sem (Grade)", min_value=0.0)
+curricular_units_2nd_sem_without_evaluations = st.number_input("Curricular Units 2nd Sem (Without Evaluations)", min_value=0)
+unemployment_rate = st.number_input("Unemployment Rate", min_value=0.0)
+inflation_rate = st.number_input("Inflation Rate", min_value=0.0)
+gdp = st.number_input("GDP", min_value=0.0)
+
+input_data = pd.DataFrame({
+    'Marital_status': [marital_status],
+    'Application_mode': [application_mode],
+    'Application_order': [application_order],
+    'Course': [course],
+    'Daytime/evening_attendance': [attendance],
+    'Previous_qualification': [previous_qualification],
+    'Previous_qualification_(grade)': [previous_qualification_grade],
+    'Nacionality': [nationality],
+    'Mother\'s_qualification': [mother_qualification],
+    'Father\'s_qualification': [father_qualification],
+    'Mother\'s_occupation': [mother_occupation],
+    'Father\'s_occupation': [father_occupation],
+    'Admission_grade': [admission_grade],
+    'Displaced': [1 if displaced == "Yes" else 0],
+    'Educational_special_needs': [1 if educational_special_needs == "Yes" else 0],
+    'Debtor': [1 if debtor == "Yes" else 0],
+    'Tuition_fees_up_to_date': [1 if tuition_fees_up_to_date == "Yes" else 0],
+    'Gender': [gender],
+    'Scholarship_holder': [1 if scholarship_holder == "Yes" else 0],
+    'Age_at_enrollment': [age_at_enrollment],
+    'International': [1 if international == "Yes" else 0],
+    'Curricular_units_1st_sem_(credited)': [curricular_units_1st_sem_credited],
+    'Curricular_units_1st_sem_(enrolled)': [curricular_units_1st_sem_enrolled],
+    'Curricular_units_1st_sem_(evaluations)': [curricular_units_1st_sem_evaluations],
+    'Curricular_units_1st_sem_(approved)': [curricular_units_1st_sem_approved],
+    'Curricular_units_1st_sem_(grade)': [curricular_units_1st_sem_grade],
+    'Curricular_units_1st_sem_(without_evaluations)': [curricular_units_1st_sem_without_evaluations],
+    'Curricular_units_2nd_sem_(credited)': [curricular_units_2nd_sem_credited],
+    'Curricular_units_2nd_sem_(enrolled)': [curricular_units_2nd_sem_enrolled],
+    'Curricular_units_2nd_sem_(evaluations)': [curricular_units_2nd_sem_evaluations],
+    'Curricular_units_2nd_sem_(approved)': [curricular_units_2nd_sem_approved],
+    'Curricular_units_2nd_sem_(grade)': [curricular_units_2nd_sem_grade],
+    'Curricular_units_2nd_sem_(without_evaluations)': [curricular_units_2nd_sem_without_evaluations],
+    'Unemployment_rate': [unemployment_rate],
+    'Inflation_rate': [inflation_rate],
+    'GDP': [gdp],
+})
+
+input_data = pd.get_dummies(input_data, drop_first=True)
+input_data = input_data.reindex(columns=scaler.get_feature_names_out(), fill_value=0)
+
+if st.button('Predict'):
+    input_scaled = scaler.transform(input_data)
+    prediction = model.predict(input_scaled)
+    predicted_class = np.argmax(prediction, axis=1)
+    st.write(f"Predicted class: {predicted_class[0]}")
+

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:a83f6a377c0aa5dd43bb95c64c1beea60b7c5c9bb6b2b8603415b1cac2dcca61
+size 2349