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Update app.py
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app.py
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from flask import Flask, render_template_string
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app = Flask(__name__)
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# Introduction content
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intro_content = """
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<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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<title>Project Introduction</title>
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<style>
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body {
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font-family: Arial, sans-serif;
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margin: 40px;
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}
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h1 {
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color: #333;
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}
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p {
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font-size: 18px;
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}
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pre {
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background: #f4f4f4;
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padding: 10px;
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border-radius: 5px;
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}
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</style>
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</head>
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<body>
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<h1>Project Title</h1>
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<p><strong>Description:</strong> A brief summary of what your project does and its main features.</p>
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<h2>Project Overview</h2>
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<p>Detailed explanation of the project, its goals, and key functionalities.</p>
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<h2>Installation</h2>
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<pre><code>
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# Step-by-step instructions on how to install your project.
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pip install your-project
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</code></pre>
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<h2>Usage</h2>
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<pre><code>
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# Instructions on how to use your project with code snippets.
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from your_project import YourModel
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model = YourModel()
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result = model.predict("Sample input")
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print(result)
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</code></pre>
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<h2>Dataset and Model Information</h2>
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<p>Information about the dataset and model architecture.</p>
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<h2>Results and Performance</h2>
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<p>Metrics, visualizations, and comparisons showcasing the performance of your model.</p>
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<h2>How to Contribute</h2>
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<p>Guidelines for contributing to your project.</p>
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<h2>License</h2>
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<p>The license for your project.</p>
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<h2>Citation</h2>
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<p>How to cite your work if applicable.</p>
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</body>
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</html>
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"""
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@app.route('/')
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def home():
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return render_template_string(intro_content)
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if __name__ == '__main__':
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app.run(debug=True)
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# import streamlit as st
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# import pandas as pd
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# import joblib
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# import streamlit as st
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# import eda
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# import models
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# page = st.sidebar.selectbox(label='Select Page:', options=['Home Page', 'Exploration Data Analysis', 'Model Prediksi'])
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# if page == 'Home Page':
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# st.header('Welcome Page')
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# st.write('')
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# st.write('Milestone 2')
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# st.write('Nama : Ryan Trisnadi')
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# st.write('Batch : ')
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# st.write('Tujuan Milestone : Prediksi harga rumah di Melbourne')
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# st.write('')
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# st.caption('Silahkan pilih menu lain di Select Box pada sebelah kiri layar anda untuk memulai!')
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# st.write('')
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# st.write('')
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# with st.expander("Latar Belakang"):
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# st.caption('Proyek ini dibuat untuk mengilustrasikan dan memproyek kedepan harga rumah di kota Melbourne, Australia. Kita akan menguji dengan Linear regression
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# dengan beberapa test algorithma seperti SVM, Decision Tree, Random Forest, dan Boosting. Oleh karena itu, kita akan memilih variable "price" atau harga
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# rumah sebagai target. Dengan mengunakan Pipeline di setiap proses dari Feature Engineering, kita bisa membuat proses lebih efisien dan mencegah data leak.
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# Dataset tersebut memiliki beberapa faktor seperti alamat, kode pos, harga rumah, lokasi lingkungan, kamar, kamar mandi, garasi, besar tanah, umur dan
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# lain-lain'.)
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# elif page == 'Exploration Data Analysis':
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# eda.run()
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# else:
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# models.run()
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