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README.md

@@ -1,20 +1 @@
----
-title: Data Analysis Dash
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Streamlit template space
-license: mit
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+# ilt-datascience

app.py

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+import streamlit as st
+import pickle
+import numpy as np
+
+# Judul aplikasi
+st.title("Aplikasi Prediksi Status Performa Mahasiswa")
+
+import streamlit as st
+
+# Input fitur-fitur
+Curricular_units_1st_sem_enrolled = st.number_input("Jumlah SKS yang Didaftarkan Mahasiswa pada Semester 1", min_value=0.0, max_value=26.0, value=0.0)
+Curricular_units_1st_sem_approved = st.number_input("Jumlah SKS yang Lulus Mahasiswa pada Semester 1", min_value=0.0, max_value=40.0, value=0.0) 
+Curricular_units_1st_sem_grade = st.number_input("Nilai Semester 1", min_value=0.0, max_value=4.0, value=0.0)
+
+Curricular_units_2nd_sem_enrolled = st.number_input("Jumlah SKS yang Didaftarkan Mahasiswa pada Semester 2", min_value=0.0, max_value=26.0, value=0.0)
+Curricular_units_2nd_sem_approved = st.number_input("Jumlah SKS yang Lulus Mahasiswa pada Semester 2", min_value=0.0, max_value=40.0, value=0.0)
+Curricular_units_2nd_sem_grade = st.number_input("Nilai Semester 2", min_value=0.0, max_value=4.0, value=0.0)
+
+# 1 Yes 0 No
+Tuition_fees_up_to_date = st.radio("Pelunasan Uang Pendidikan (Iya (1); Tidak (0))", ("1", "0"))
+# 1 Yes 0 No
+Scholarship_holder = st.radio("Penerima Beasiswa (Iya (1); Tidak (0))", ("1", "0"))
+Admission_grade = st.number_input("Nilai Penerimaan", min_value=0.0, max_value=200.0, value=0.0)
+Displaced = st.radio("Apakah Mahasiswa Orang Terlantar? (Iya (1); Tidak (0))", ("1", "0"))
+
+
+# Data dalam bentuk list
+data = [
+    [
+        Curricular_units_2nd_sem_approved,
+        Curricular_units_2nd_sem_grade,
+        Curricular_units_1st_sem_approved,
+        Curricular_units_1st_sem_grade,
+        Tuition_fees_up_to_date,
+        Scholarship_holder,
+        Curricular_units_2nd_sem_enrolled,
+        Curricular_units_1st_sem_enrolled,
+        Admission_grade,
+        Displaced
+    ]
+]
+
+# Load model dan skaler yang telah disimpan sebelumnya
+scaler = pickle.load(open('scaler.pkl', 'rb'))
+best_model = pickle.load(open('model_rf.pkl', 'rb'))
+
+# Ketika tombol "Prediksi" ditekan
+if st.button("Prediksi"):
+    # Standardisasi data
+    data_scaled = scaler.transform(data)
+
+    # Prediksi hasil Status
+    hasil_prediksi = best_model.predict(data_scaled)
+    hasil_prediksi = int(hasil_prediksi)
+
+    # Mapping hasil prediksi ke label yang sesuai
+    if hasil_prediksi == 0:
+        status = "Dropout"
+    elif hasil_prediksi == 1:
+        status = "Enrolled"
+    else:
+        status = "Graduate"
+
+    # Menampilkan hasil prediksi
+    st.write(f"Hasil Prediksi Status: {status}")

model_rf.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4e717695173b543ad85c8e2e898e3b6614fde258b31d1c3d5a51f0e1f7d8e39f
+size 27077426

requirements.txt

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-altair
+numpy
 pandas
-streamlit
+streamlit
+scikit-learn
+matplotlib
+seaborn

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2d497d01dd1104ed86f5cca64c3c60a7dbbe5ec073c0ca109d6334cb950ce50e
+size 1064