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.main.py@neomake_200149_115.py
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.main.py@neomake_200149_115.py
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import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
st.write("""
# Program Prediksi Peluang Penerimaan Program S2
""")
st.write( """
## Keterangan Data Yang Digunakan
1. GRE Score: Merupakan Score Test Untuk Masuk Program S2 (0 - 340) Bersifat Continous
2. TOEFL Score: Score Kemampuan TOEFL (0 - 120) Bersifat Continous
3. University Rating: Rating Universitas (0 - 5) Bersifat Ordinal
4. Kekuatan Surat Rekomendasi (0 - 5) Bersifat Ordinal
5. GPA Sewaktu Undergraduate (0 - 10) Bersifat Continous
6. Pengalaman Riset (0 : tidak ada, 1 : ada) Bersifat Nominal
7. Peluang Diterima (0 - 1) Merupakan Dependent Variable
""")
st.write("""
## Overview Data
""")
myData = pd.read_csv('data.csv')
st.dataframe(myData)
st.write("""
## Deskripsi Data
""")
st.dataframe(myData.describe())
# Preproccessing Data
# Memisahkan Label Dan Fitur
X = myData.iloc[:, :-1].values
y = myData.iloc[:, -1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=69)
# Melakukan Scaling
X_train_ss_scaled = X_train
X_test_ss_scaled = X_test
X_train_mms_scaled = X_train
X_test_mms_scaled = X_test
y_train_ss_scaled = y_train
y_test_ss_scaled = y_test
y_train_mms_scaled = y_train
y_test_mms_scaled = y_test
from sklearn.preprocessing import StandardScaler
ss_train_test = StandardScaler()
X_train_ss_scaled[:, :-1] = ss_train_test.fit_transform(X_train[:, :-1])
X_test_ss_scaled[:, :-1] = ss_train_test.transform(X_test[:, :-1])
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
l_regressor_ss = LinearRegression()
l_regressor_ss.fit(X_train_ss_scaled, y_train)
y_pred_l_reg_ss = l_regressor_ss.predict(X_test_ss_scaled)
st.write("Dengan Menggunakan Multiple Linear Regression Diperoleh Skor Untuk Data Test")
st.write(r2_score(y_test, y_pred_l_reg_ss))
st.write("# Sekarang Silahkan Masukan Skor Test Kamu Untuk Mengetahui Prediksi Peluang Kelulusan S2 Kamu")
form = st.form(key='my-form')
inputGRE = form.number_input("Masukan GRE Score: ", 0)
inputTOEFL = form.number_input("Masukan TOEFL Score: ", 0)
inputUnivRating = form.number_input("Masukan Rating Univ: ", 0)
inputSOP = form.number_input("Masukan Kekuatan SOP: ", 0)
inputLOR = form.number_input("Masukan Kekuatan LOR: ", 0)
inputCGPA = form.number_input("Masukan CGPA: ", 0)
inputResearch = form.number_input("Pengalaman Researc, 1 Jika Pernah Riset, 0 Jika Tidak", 0)
submit = form.form_submit_button('Submit')
completeData = np.array([[inputGRE, inputTOEFL, inputUnivRating,
inputSOP, inputLOR, inputCGPA, inputResearch]])
for i in completeData:
for j in i:
st.write(type(j))
scaledData = ss_train_test.fit_transform(completeData)
st.write(completeData)
st.write(scaledData)
st.write('Tekan Submit Untuk Melihat Prediksi Peluang S2 Anda')
if submit:
prediction = l_regressor_ss.predict(completeData)
st.write(prediction)